Source: https://patents.google.com/patent/JPWO2016147944A1/en
Timestamp: 2020-04-02 20:32:15
Document Index: 53453869

Matched Legal Cases: ['art 144', 'art 143', 'art 144', 'art 144', 'art 150', 'art 150', 'art 131', 'art 132', 'art 140', 'art 141', 'art 142', 'art 143', 'art 144', 'art 150', 'art 151', 'art 152']

JPWO2016147944A1 - Malware-infected terminal detection device, malware-infected terminal detection system, malware-infected terminal detection method, and malware-infected terminal detection program - Google Patents
Malware-infected terminal detection device, malware-infected terminal detection system, malware-infected terminal detection method, and malware-infected terminal detection program Download PDF
JPWO2016147944A1
JPWO2016147944A1 JP2016057119A JP2017506466A JPWO2016147944A1 JP WO2016147944 A1 JPWO2016147944 A1 JP WO2016147944A1 JP 2016057119 A JP2016057119 A JP 2016057119A JP 2017506466 A JP2017506466 A JP 2017506466A JP WO2016147944 A1 JPWO2016147944 A1 JP WO2016147944A1
JP2016057119A
JP6348656B2 (en
2017-08-31 Publication of JPWO2016147944A1 publication Critical patent/JPWO2016147944A1/en
2018-06-27 Publication of JP6348656B2 publication Critical patent/JP6348656B2/en
The detection device (100) generates an event sequence formed based on the occurrence order of the event from the event acquired for each identifier for distinguishing the terminal or malware of the monitoring target network. Then, the detection device (100) extracts an event that appears in common among event sequences belonging to the same cluster in a cluster formed by event sequences having a certain degree of similarity, and appears from the similar common event sequence. A representative event series consisting of relationships between frequently occurring events is extracted as a detection event series. Then, the detection apparatus (100) detects malware infection of the terminal of the monitoring target network based on the match between the event series based on the generated communication of the monitoring target network and the extracted detection event series.
The present invention relates to a malware-infected terminal detection device, a malware-infected terminal detection system, a malware-infected terminal detection method, and a malware-infected terminal detection program.
In recent years, malicious programs (hereinafter referred to as “malware”) that cause threats such as information leaks and unauthorized access have become increasingly popular. Malware receives instructions from an attacker via a server or the like after infection, and causes threats such as attacks and information leaks. Recent malware takes a technique of disguising communication with an attacker as regular communication (for example, see Non-Patent Document 1).
The increase in the number of discovered malware is also remarkable, and it has been reported that one new malware appears in a few seconds (for example, refer nonpatent literature 2). For this reason, it is not possible to prevent malware threats only by measures on the host side such as anti-virus software. Therefore, a technique for reducing the threat of malware by analyzing communication data and identifying a terminal infected with malware has attracted attention (for example, see Non-Patent Document 3).
As a technique for detecting a terminal infected with malware, a technique for patterning communication characteristics of a terminal infected with malware and detecting a terminal infected with malware is known (for example, see Patent Document 1). An example of a method for detecting a terminal infected with malware is to analyze communication data as an analysis target, pattern communication data obtained by malware analysis, and check whether a similar pattern appears in the communication of the monitored network (NW) This is a technique for detecting terminals infected with malware.
Japanese Patent No. 5009244
Advanced Cyber Attack Trends, [online], [Search September 4, 2014], Internet <URL: http://www.fireeye.com/en/resources/pdfs/fireeye-advanced-cyber- attack-landscape.pdf> Annual Report Pandalabs 2013 summary, [online], [search September 3, 2014], Internet <URL: http://press.pandasecurity.com/wp-content/uploads/2010/05/PandaLabs-Annual-Report_2013 .pdf> Sebastian Garcia et al., Survey on network-based botnet detection methods, Security and communication networks 2013, [online], [March 13, 2014 search], Internet <URL: http://onlinelibrary.wiley.com/doi/ 10.1002 / sec.800 / full>
However, the above conventional technique has the following problems. That is, as described above, since the number of malwares is enormous in recent years, if all communication of all malware is patterned, the number of patterns becomes enormous, and it is determined whether or not the pattern exists in the communication of the monitored network. It takes a long time to do. Further, in the above-described conventional technology, a state is defined for each communication payload, and the state transition is used as a pattern. Therefore, a new pattern is generated only by malware communicating with a different communication payload. Further, since communication similar to that of a non-infected terminal is also confirmed in malware communication, using all communication patterns for detection in the communication of the monitored network will induce false detection.
Accordingly, an object of the present invention is to provide an apparatus, a method, and a program for solving the above-described problems and detecting a malware-infected terminal.
In order to solve the above-described problems and achieve the object, the malware-infected terminal detection apparatus of the present invention is an event that is an event that matches a rule that characterizes communication among communication of a monitored network and communication generated by malware. In addition, a sequence generation unit that generates an event sequence formed based on an occurrence order of the event from an event acquired for each identifier that distinguishes a terminal or malware of a monitored network, and communication generated by malware In a cluster formed by event sequences with a certain degree of similarity between event sequences based on, events that appear in common between event sequences belonging to the same cluster are extracted, and the extracted events are combined in chronological order Extract event series longer than the length as a common event series, Generated by the sequence generation unit, a detection sequence extraction unit that extracts, as a detection event sequence, a representative event sequence consisting of relationships between events having a high appearance frequency from a number of the common event sequences that are similar to each other If it is determined that the event sequence based on the communication of the monitored network and the detection event sequence extracted by the detection sequence extraction unit match, a malware-infected terminal is included in the monitored network. And a detection unit that detects the presence.
The malware-infected terminal detection system of the present invention is a malware-infected terminal detection system comprising a malware execution environment, a monitoring target network, and a malware-infected terminal detection device, the malware-infected terminal detection device. Is an event that is an event that matches a rule characterizing communication among communication of the monitored network and communication generated by malware executed in the malware execution environment, and distinguishes a terminal or malware of the monitored network An event sequence having a certain degree of similarity between a sequence generation unit that generates an event sequence formed based on the occurrence order of the event from events acquired for each identifier, and an event sequence based on communication generated by malware In a cluster formed by Events that appear in common among event sequences belonging to a cluster are extracted, and event sequences of a certain length or longer combined with the extracted events in chronological order are extracted as common event sequences, and similar among the multiple common event sequences Based on the communication of the monitoring target network generated by the sequence generation unit and the detection sequence extraction unit that extracts, as the detection event sequence, a representative event sequence composed of relationships between frequently occurring events from the common event sequence Detection that detects the presence of a malware-infected terminal in the monitored network when it is determined that the event sequence matches the detection event sequence extracted by the detection sequence extraction unit And a portion.
The malware-infected terminal detection method of the present invention is a malware-infected terminal detection method executed by a malware-infected terminal detection system having a malware execution environment, a monitored network, and a malware-infected terminal detection device. Among the communication of the monitored network and the communication generated by the malware executed in the malware execution environment, the event is an event that matches the rule characterizing the communication, and distinguishes the terminal or malware of the monitored network Events that have a certain degree of similarity between a sequence generation process that generates an event sequence that is formed based on the occurrence order of the event from events acquired for each identifier and communication that is generated by malware In clusters formed by series, Extract events that appear in common among event sequences belonging to one cluster, extract event sequences of a certain length or more that combine the extracted events in chronological order, and extract multiple common event sequences. From the similar common event series, a detection sequence extraction process for extracting a representative event series consisting of relationships between events having a high appearance frequency as a detection event series, and communication of the monitoring target network generated by the series generation process When it is determined that the event series based on the detected event series matches the detection event series extracted by the detection series extraction step, it detects that a malware-infected terminal exists in the monitored network And a detection step.
In addition, the malware-infected terminal detection program of the present invention is an event that is an event that matches a rule that characterizes the communication among the communication of the monitored network and the communication generated by the malware. A sequence generation step for generating an event sequence formed based on the occurrence order of the event from the event acquired for each identifier to be distinguished, and a similarity between the event sequence based on communication generated by malware is not less than a certain level In a cluster formed by event sequences, events that appear in common among event sequences belonging to the same cluster are extracted, and event sequences longer than a certain length that combine the extracted events in chronological order are extracted as common event sequences A plurality of the common event series A detection sequence extracting step for extracting, as a detection event sequence, a representative event sequence composed of relationships between events having a high frequency of appearance from the common event sequence similar to a person, and a network to be monitored generated by the sequence generation step. If it is determined that the event series based on communication matches the detection event series extracted in the detection series extraction step, it is determined that a malware-infected terminal exists in the monitored network. A detection step for detecting is executed by the computer.
According to the present invention, it is possible to reduce the pattern to be collated in the monitoring target NW, reduce the time required for collation, and reduce the situation of erroneously detecting communication that normally occurs in the monitoring target NW. .
FIG. 1 is a configuration diagram illustrating an outline of a detection device according to an embodiment. FIG. 2 is a diagram illustrating an example of the monitoring target NW analysis result according to the embodiment. FIG. 3 is a diagram illustrating an example of a malware communication analysis result according to the embodiment. FIG. 4 is a diagram illustrating a configuration example of the detection device according to the embodiment. FIG. 5 is a diagram illustrating an example of a common event sequence according to the embodiment. FIG. 6 is a diagram illustrating an example of an event series graph generated from the common event series according to the embodiment. FIG. 7 is a diagram illustrating an example of a simple path generated from the event series graph according to the embodiment. FIG. 8 is a diagram illustrating an example of the common event sequence and the representative event sequence according to the embodiment. FIG. 9 is a diagram illustrating an example of processing in the event matching unit according to the embodiment. FIG. 10 is a flowchart illustrating an excluded event extraction processing procedure by the excluded event extraction unit. FIG. 11 is a flowchart showing an event sequence generation processing procedure by the event sequence generation unit. FIG. 12 is a flowchart showing a common event sequence extraction processing procedure by the common event sequence extraction unit. FIG. 13 is a flowchart illustrating a representative event sequence extraction processing procedure by the representative event sequence extraction unit. FIG. 14 is a flowchart illustrating a candidate determination processing procedure performed by the event matching unit and the candidate determination unit. FIG. 15 is a flowchart illustrating a detection processing procedure by the detection unit. FIG. 16 is a flowchart showing a verification processing procedure by the event verification unit. FIG. 17 is a diagram illustrating a computer that executes a malware-infected terminal detection program.
Embodiments of a malware-infected terminal detection device, a malware-infected terminal detection system, a malware-infected terminal detection method, and a malware-infected terminal detection program according to the present application will be described below in detail with reference to the drawings. Note that the embodiment of the present invention is not limited to the malware-infected terminal detection device, the malware-infected terminal detection system, the malware-infected terminal detection method, and the malware-infected terminal detection program according to this embodiment.
First, an outline of processing performed by the detection apparatus 100 that is a detection apparatus for a malware-infected terminal will be described with reference to FIG. FIG. 1 is a configuration diagram illustrating an outline of a detection device 100 according to an embodiment. As illustrated in FIG. 1, the processing by the detection device 100 is executed by a sequence generation unit 130, a detection sequence extraction unit 140, and a detection unit 150 included in the detection device 100. The detection apparatus 100 generates a detection event sequence from the monitoring target NW (Network) analysis result (for sequence extraction) and the malware communication analysis result collected in advance before the detection, and the monitoring target NW analysis result By comparing the event sequence generated from (for detection) and the event sequence for detection, a terminal (host) infected with malware in the monitoring target NW is detected.
Here, the monitoring target NW analysis result (for series extraction and detection) stores data having fields for an identifier for identifying a host in the monitoring target NW, an event, and an event occurrence time. The event means an event that captures each feature when a certain feature is confirmed in the communication. For example, an event is an event that communication with a specific communication destination was included by analysis of a device log recorded in Firewall, WebProxy, etc., or a certain number of times of communication was performed within a predetermined time An event, an event that a malicious data transmission is detected by IDS (Intrusion Detection System), and the like are applicable. That is, the event corresponds to an event that matches a rule characterized by a high probability of malignant communication among the communications in the monitoring target NW. For example, the detection apparatus 100 analyzes whether a rule that characterizes communication is met by a predetermined external device, and acquires an event determined to match the rule as a monitoring target NW analysis result. The malware communication analysis result is a result of analyzing communication data when malware is actually operated in a malware execution environment such as a sandbox from the same viewpoint as when the above-mentioned monitoring target NW analysis result was acquired. is there. In addition, the event series is a series of monitoring target NW analysis results arranged in time series for each host of the monitoring target NW, or a malware communication analysis result arranged in time series for each malware sample.
Here, an example of the monitoring target NW analysis result is shown using FIG. FIG. 2 is a diagram illustrating an example of the monitoring target NW analysis result according to the embodiment. As shown in FIG. 2, the event detected for each identifier of the monitoring target NW host stores the event type and the event occurrence time in association with each other. For example, in FIG. 2, an event “communication detection with a specific communication destination” is “October 15, 2014 12:20:12” on the host identified by “192.168.10.11.” An example has occurred. Next, an example of a malware communication analysis result is shown using FIG. FIG. 3 is a diagram illustrating an example of a malware communication analysis result according to the embodiment. As shown in FIG. 3, for the event detected for each malware identifier, the event type and the event occurrence time are stored in association with each other as in the monitoring target NW analysis result.
Below, the process which the detection apparatus 100 performs is demonstrated along a flow. The sequence generation unit 130 according to the detection apparatus 100 includes an excluded event extraction unit 131 and an event sequence generation unit 132, and generates an event sequence for each of the monitoring target NW analysis result and the malware communication analysis result as inputs. . The sequence generation unit 130 is an event that is an event that matches a rule characterizing the communication among the communication of the monitoring target network and the communication generated by the malware, and is acquired for each identifier that distinguishes the terminal of the monitoring target network or the malware. From the event, an event sequence formed based on the order of occurrence of the event is generated.
Specifically, when the monitoring target NW analysis result (for series extraction) is input, the exclusion event extraction unit 131 excludes events that have been confirmed by many hosts in the monitoring target NW in the analysis result. And set. Although the false detection rate can be reduced by providing the exclusion event extraction unit 131, the detection apparatus 100 may be configured not to provide the exclusion event extraction unit 131.
The event sequence generation unit 132 generates an event sequence including events that do not correspond to the excluded event, from the monitoring target NW analysis result and the malware communication analysis result. Generally, since there are few infected terminals in the monitoring target NW, it can be determined that events confirmed by many hosts do not capture the characteristics of communication by malware. Therefore, the event sequence generation unit 132 can generate an event sequence by excluding events that are confirmed on terminals that are not infected with malware by excluding excluded events. That is, according to the event sequence generation unit 132, it is possible to reduce false detection in detection of an infected terminal.
In addition, the event sequence generation unit 132 generates one event sequence for events of the same host or the same malware from events whose event occurrence interval is within a predetermined time. That is, the event sequence generation unit 132 generates an event sequence by dividing a series of events related to the operation of the malware. Furthermore, the event sequence generation unit 132 generates an event sequence by excluding duplicate events from events of the same host or the same malware.
The process of the event sequence generation unit 132 will be described with a specific example. For example, it is assumed that event A, event B, and event C are confirmed in the order of “ABCABCAA” as an analysis result of a certain host. For example, event A is an event indicating access to a specific server, event B is an event indicating that a file is downloaded from a specific server, and event C is a file downloaded by event B. This event indicates that a predetermined server has been accessed based on the event. At this time, the event series generation unit 132 excludes duplicate events from a series of events “ABCABCAA”. That is, the event sequence generation unit 132 generates “ABC” as an event sequence from “ABCABCAA”. That is, the event series generation unit 132 adds events of a certain host as elements of the event series in order from the earliest occurrence time, and does not add events confirmed after the second time to the event series.
As a result, the event sequence generation unit 132 calculates the difference in the number of repetitions even when repeated communication occurs due to the execution timing of the malware or the command from the C & C (Command and Control) server. Absorbed event sequences can be generated. That is, according to the event series generation unit 132, it is possible to improve the accuracy in the detection process described later.
Next, processing of the detection sequence extraction unit 140 according to the detection apparatus 100 will be described. The detection sequence extraction unit 140 includes a common event sequence extraction unit 141, a representative event sequence extraction unit 142, an event matching unit 143, and a candidate determination unit 144. Based on the event sequence generated by the sequence generation unit 130, Extract detection event series.
The detection sequence extraction unit 140 is an event that appears in common among event sequences that belong to the same cluster in a cluster formed by event sequences having a certain degree of similarity between event sequences based on communication generated by malware. Event sequences longer than a certain length that are obtained by combining the extracted events in chronological order are extracted as common event sequences, and the relationship between events that occur frequently from common event sequences that are similar among multiple common event sequences A representative event sequence consisting of is extracted as a detection event sequence.
Specifically, the common event sequence extraction unit 141 performs clustering after calculating the similarity between the event sequences extracted from the malware communication analysis result. After that, the common event series extraction unit 141 extracts events that are confirmed in common among the event series among the event series having a certain degree of similarity or more, taking the order into consideration, and sets the common event series.
In the representative event series extraction unit 142, among the common event series extracted by the common event series extraction unit 141, a representative event series including a relationship between events having a high appearance frequency from similar common event series is used as a detection event series candidate. Extract. A detailed representative event sequence extraction method will be described later.
The event collation unit 143 collates the event sequence of the monitoring target NW analysis result (for sequence extraction) with the detection event sequence candidate, and determines how much each detection event sequence candidate can detect the host in the monitoring target NW. calculate. The event matching unit 143 is the ratio of the length of the matching portion between the determination target event sequence that is an event sequence based on the communication of the monitoring target network and the detection event sequence candidate to the length of the detection event sequence candidate. And the second match rate, which is a ratio of the length of the detection event sequence candidate to the length of the longest common event sequence in the cluster to which the detection event sequence candidate belongs, If it is equal to or greater than the threshold value, it is determined that the determination target event sequence matches the detection event sequence candidate.
Based on the number of detection hosts for each detection event sequence candidate calculated by the event matching unit 143, the candidate determination unit 144 detects the detection event sequence when the ratio of the number of detection hosts to the total number of hosts of the monitoring target NW is equal to or less than a certain value. Candidates are output as a detection event sequence.
Subsequently, processing of the detection unit 150 according to the detection apparatus 100 will be described. The detection unit 150 includes an event matching unit 151 and a detection result output unit 152, and detects a malware-infected terminal in the monitoring target NW. When the detection unit 150 determines that the event sequence based on the communication of the monitoring target network generated by the sequence generation unit 130 matches the detection event sequence extracted by the detection sequence extraction unit 140 In addition, it detects that a malware-infected terminal exists in the monitored network.
Specifically, the event matching unit 151 matches the event sequence generated from the monitoring target NW analysis result (for detection) and the detection event sequence, similarly to the event matching unit 143 of the detection sequence extracting unit 140. Check whether or not. The event matching unit 151 includes a first match that is a ratio of the length of the matching portion between the determination target event sequence that is an event sequence based on the communication of the monitoring target network and the detection event sequence to the length of the detection event sequence. The value obtained by multiplying the rate and the second match rate, which is the ratio of the length of the detection event sequence to the length of the longest common event sequence in the cluster to which the detection event sequence belongs, is equal to or greater than a predetermined threshold. In this case, it is determined that the determination target event sequence matches the detection event sequence.
The detection result output unit 152 outputs host information determined to match the detection event sequence as a result of the collation by the event collation unit 151. The host information is, for example, an IP (Internet Protocol) address of a terminal in the monitoring target NW.
As described above, the detection apparatus 100 generates a detection event sequence from the monitoring target NW analysis result (for sequence extraction) and the malware communication analysis result, and generates the event sequence and detection detection from the monitoring target NW analysis result (for detection). A terminal infected with malware is detected in the monitoring target NW by collating with the event series.
As described above, the detection apparatus 100 according to the embodiment is a time-series pattern selected as a more representative one among the time-series patterns of common characteristics that characterize malware among a plurality of malware communications. Infected terminals are detected using only a certain event sequence for detection. For this reason, according to the detection apparatus 100, the pattern which should be collated with the monitoring object NW can be reduced, and the time concerning collation can be reduced. Furthermore, since the detection apparatus 100 uses a detection event sequence from which events or time series of events that can be observed in the monitoring target NW in advance are excluded from processing, a situation in which communication that normally occurs in the monitoring target NW is erroneously detected is detected. Can be reduced.
Note that the detection apparatus 100 may use only the malware communication analysis result in generating the detection event series, without using the monitoring target NW analysis result (for series extraction). Details of the processing related to the detection apparatus 100 will be described later using a flowchart.
Next, the detection apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram illustrating a configuration example of the detection apparatus 100 according to the embodiment.
As illustrated in FIG. 4, the detection apparatus 100 according to the embodiment includes an IF (interface) unit 110, an event sequence storage unit 120, a detection event sequence storage unit 121, a sequence generation unit 130, and a detection A series extraction unit 140 and a detection unit 150 are included.
The IF unit 110 is, for example, a NIC (Network Interface Card) or the like, and transmits / receives various data to / from an external device. For example, the IF unit 110 receives a result obtained by analyzing a firewall or WebProxy device log installed in the monitoring target NW as the monitoring target NW analysis result.
The event sequence storage unit 120 and the detection event sequence storage unit 121 are realized by, for example, a semiconductor memory device such as a random access memory (RAM) or a flash memory, a hard disk, an optical disk, or the like. The event sequence storage unit 120 and the detection event sequence storage unit 121 appropriately store information handled by the sequence generation unit 130, the detection sequence extraction unit 140, and the detection unit 150.
For example, the event sequence storage unit 120 stores the event sequence generated by the sequence generation unit 130. In addition, the detection event sequence storage unit 121 stores the detection event sequence extracted by the detection sequence extraction unit 140. The detection apparatus 100 does not require the event sequence storage unit 120 or the detection event sequence storage unit 121 as a component. For example, the detection device 100 may use an external storage device that performs the same processing as the event sequence storage unit 120 or the detection event sequence storage unit 121.
The sequence generation unit 130, the detection sequence extraction unit 140, and the detection unit 150 are realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). In addition, the sequence generation unit 130, the detection sequence extraction unit 140, and the detection unit 150 are configured such that, for example, a program stored in a storage device (not illustrated) is stored in a RAM by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. This is realized by being executed as a work area.
The sequence generation unit 130 includes an excluded event extraction unit 131 and an event sequence generation unit 132, and generates an event sequence for each of the monitoring target NW analysis result and the malware communication analysis result as inputs. When the monitoring target NW analysis result (for series extraction) is input, the excluded event extraction unit 131 sets an event confirmed by many hosts in the monitoring target NW in the analysis result as an excluded event. Specifically, the excluded event extraction unit 131 acquires the total number of hosts in the monitoring target NW included in the input monitoring target NW analysis result (for series extraction) and the number of hosts including a predetermined event. . Subsequently, the excluded event extraction unit 131 excludes the predetermined event when the number of hosts including the predetermined event exceeds a certain ratio based on the ratio between the number of hosts including the predetermined event and the total number of hosts. Set as event. As a result, the excluded event extraction unit 131 can generate an event sequence only with events that exclude general processing performed in many hosts.
The event sequence generation unit 132 generates an event sequence including events that do not correspond to the excluded event, from the monitoring target NW analysis result and the malware communication analysis result. Specifically, the event series generation unit 132 acquires, as an input, an event that does not correspond to the excluded event from the monitoring target NW analysis result or the malware communication analysis result. At this time, the event series generation unit 132 records the event occurrence time of the read event. Then, the event series generation unit 132 determines whether the recorded event occurrence time is a predetermined time or more away from the event occurrence time read immediately before. Then, when the event occurrence time is not separated from the previous event time by a certain time or more, the event series generation unit 132 estimates that the event is an element of the same event series as the previous event, and has been determined. Generate events as an event series. As described above, the event sequence generation unit 132 generates an event sequence based on an event excluding general processing, and thus it is possible to reduce false detection in detection of an infected terminal.
The detection sequence extraction unit 140 includes a common event sequence extraction unit 141, a representative event sequence extraction unit 142, an event matching unit 143, and a candidate determination unit 144. Based on the event sequence generated by the sequence generation unit 130, Extract detection event series.
The common event series extraction unit 141 extracts a common event series from the event series generated by the series generation unit 130. Specifically, the common event sequence extraction unit 141 performs clustering after calculating the similarity between the event sequences extracted from the malware communication analysis result, and in the event sequences having a certain degree of similarity or more, each event Extract events that are commonly confirmed between series, taking the order into account. The common event sequence extraction unit 141 includes events that are commonly confirmed when the events are arranged in chronological order and the length of the commonly confirmed event is longer than a predetermined length. The event series is a common event series.
The representative event series extraction unit 142 uses, as a detection event series candidate, a representative event series that includes relationships between events that appear frequently from similar common event series among the common event series extracted by the common event series extraction unit 141. Extract. Specifically, the representative event sequence extraction unit 142 generates a directed graph from the common event sequence, in which the event is a node, the occurrence order between events is an edge, and the number of appearances of the event context is the edge weight. The sum of the weights is calculated for each simple path, and the simple road showing the maximum weight is set as the representative event sequence.
First, an example of a common event sequence for extracting a representative event sequence will be described with reference to FIG. FIG. 5 shows a plurality of common event sequences each extracted from one cluster. Further, the label in FIG. 5 indicates an event. Furthermore, the arrows between events in FIG. 5 indicate the order in which events occur. Further, since the common event series 1, 4 and 5 in FIG. 5 are exactly the same event series, the representative event series may be extracted after being unique to one event series.
Here, as shown in FIG. 5, a certain common event sequence may appear as a part of another common event sequence. For example, the common event series “ABCD” of the common event series 1 in FIG. 5 appears as a part of the common event series 2 and 3. In addition, a case where a part of an event of a certain common event series is replaced may be another common event series. For example, there may be “ABC2EF” or “ABC3EF” in which “1” is replaced with “2” or “3” in the common event series “ABC1EF”.
A method for extracting a representative event sequence in the representative event sequence extracting unit 142 will be described on the premise that there is a similar sequence as described above in the common event sequence in the same cluster. First, the representative event series extraction unit 142 designates a cluster to be extracted, and generates an event series graph from the common event series included in the designated cluster. Here, the event series graph is a directed graph having an event as a node, an event context as an edge, and an event context appearance frequency as an edge weight. The representative event series extraction unit 142 assigns a node meaning them to the beginning and the end of the event series graph.
A specific example of the event series graph will be described with reference to FIG. FIG. 6 is a diagram illustrating an example of an event series graph generated from the common event series according to the embodiment. As shown in FIG. 6, the representative event series extraction unit 142 represents events as nodes, arrows indicating the order in which events occur as edges, and the number of appearances of the context of events as edge weights. Further, the node 11 having the label “START” indicates the head of the event series graph. A node 18 labeled “END” indicates the end of the event series graph.
First, as illustrated in FIG. 7, the representative event sequence extraction unit 142 generates an event sequence graph, and then extracts a simple path from the start point to the end point of the event sequence graph. FIG. 7 is a diagram illustrating an example of a simple path generated from the event series graph according to the embodiment. Next, the representative event sequence extraction unit 142 determines whether a representative event sequence can be extracted for each extracted simple road. Hereinafter, the procedure in which the representative event series extraction unit 142 extracts the representative event series will be described in detail.
At this time, the representative event series extraction unit 142 may set an event in which the number of appearances of the context of the event is a certain number or more as a node of the directed graph. For example, in the example of FIG. 7, when the fixed number of times is set to 2, the number of appearances of the context of the node 17 is 1, which is not more than the fixed number of times, and thus is not included in the event series graph nodes.
First, for each simple road, the representative event series extraction unit 142 sets the top of the graph as “event (front)” and the next event as “event (back)”. Next, the representative event sequence extraction unit 142 sets the skip flag to false and initializes the weight of the representative event sequence to 0. Then, the representative event series extraction unit 142 sets in advance a value obtained by dividing the number of occurrences between “event (before)” and “event (after)” by the number of common event series that is the basis of the event series graph. It is determined whether or not it is equal to or greater than the threshold value.
First, the value obtained by dividing the number of occurrences between “event (before)” and “event (after)” by the number of common event sequences that are the basis of the event sequence graph is equal to or greater than a preset threshold value. The case will be described. In this case, the representative event series extraction unit 142 determines whether or not the skip flag is true. When the skip flag is true, the representative event series extraction unit 142 selects “event (previous)” as an element of the representative series, and sets the skip flag to false. Then, “event (after)” is selected as an element of the representative event series, and the number of occurrences of “event (before)” and “event (after)” is added to the weight of the representative event series. In the first case, the skip flag is always false, but since these processes are repeated for each event, both the case where the skip flag is false and the case where it is true are considered after the second time. It is done.
Second, the value obtained by dividing the number of occurrences between “event (before)” and “event (after)” by the number of common event sequences that are the basis of the event sequence graph is smaller than a preset threshold value. The case will be described. In this case, the representative event series extraction unit 142 sets the skip flag to true.
After that, when “event (after)” matches the end point of the graph, the representative event series extraction unit 142 performs the next simple road determination. If the event does not match the end point, the representative event series extraction unit 142 sets “event (after)” to “event (before)” and sets the next event after “event (after)” to a new “event (after). ) ”And continue processing. Then, the representative event sequence extraction unit 142 outputs all the representative event sequences having the largest weight as the representative event sequence of the cluster after determining all the simple roads. The representative event sequence extraction unit 142 may output all of them as representative event sequences when there are a plurality of items having the maximum weight, or the number of events included in the representative event sequence is the largest. Alternatively, the smallest number may be output. Further, when the ratio of the representative event sequence length to the maximum value of the common event sequence length belonging to the cluster is smaller than a predetermined value, the representative event sequence extraction unit 142 sets the common event sequence as the representative event sequence. Good.
With reference to FIG. 7, the procedure by which the representative event series extraction unit 142 extracts the representative event series will be described with a specific example. First, an example in which determination is performed on the simple road 10a will be described. First, the representative event series extraction unit 142 sets the node 11a to “event (front)” and the node 12a to “event (back)”. Next, the representative event sequence extraction unit 142 sets the skip flag to false, the threshold value to 0.3, and initializes the weight of the representative event sequence to 0. The weight of the edge 21a indicating the number of occurrences between the node 11a and the node 12a is 5, and a value obtained by dividing this by 5 which is the number of common event sequences that are the basis of the event sequence graph is 1. Therefore, the representative event series extraction unit 142 determines that the calculated value is greater than or equal to the threshold value.
Since the skip flag is false, the representative event sequence extraction unit 142 does not select the node 11a as a representative sequence element and does not change the skip flag from false. Then, the representative event sequence extraction unit 142 selects the node 12a as an element of the representative event sequence, and adds the weight 5 of the edge 21a indicating the number of occurrences of the node 11a and the node 12a to the weight of the representative event sequence.
Subsequently, the representative event series extraction unit 142 repeats the same process for the nodes 12a and 13a, the nodes 13a and 14a, and the nodes 14a and 15a. Then, the representative event series extraction unit 142 sets the node 15a to “event (front)” and the node 18a to “event (back)”. Next, the weight of the edge 25a indicating the number of occurrences between the node 15a and the node 18a is 3, and a value obtained by dividing the weight by 5 that is the number of common event sequences on which the event sequence graph is based is 0.6. Therefore, the representative event series extraction unit 142 determines that the calculated value is equal to or greater than the threshold value.
Then, the node 18a which is “event (after)” is selected as an element of the representative event series, and the edge 25a indicating the number of occurrences of the node 15a which is “event (before)” and the node 18a which is “event (after)”. Is added to the weight of the representative event sequence. Since the “event (after)” matches the node 18a that is the end point of the graph, the representative event sequence extraction unit 142 ends the determination of the simple road 10a, and determines the next simple road. At this time, the weights of the edges 21a, 22a, 23a, 24a, and 25a are added to the weights of the representative event series, and the total weight of the simple road 10a is 23. At this time, the representative event sequence extracted from the simple road 10a is “ABCD”.
Next, an example in which determination is performed on the simple road 10b will be described. First, the representative event series extraction unit 142 sets the node 11b to “event (front)” and the node 12b to “event (back)”. Next, the representative event sequence extraction unit 142 sets the skip flag to false, the threshold value to 0.3, and initializes the weight of the representative event sequence to 0. Then, the weight of the edge 21b indicating the number of occurrences between the node 11b and the node 12b is 5, and a value obtained by dividing this by 5 which is the number of common event sequences on which the event sequence graph is based is 1. Therefore, the representative event series extraction unit 142 determines that the calculated value is greater than or equal to the threshold value.
Since the skip flag is false, the representative event sequence extraction unit 142 does not select the node 11b as an element of the representative sequence, and does not change the skip flag from false. Then, the representative event sequence extracting unit 142 selects the node 12b as an element of the representative event sequence, and adds the weight 5 of the edge 21b indicating the number of occurrences of the node 11b and the node 12b to the weight of the representative event sequence.
Subsequently, the representative event series extraction unit 142 repeats the same processing for the nodes 12b and 13b, the nodes 13b and 14b, the nodes 14b and 15b, and the nodes 15b and 16b. Then, the representative event series extraction unit 142 sets the node 16b to “event (front)” and the node 18b to “event (back)”. Next, the weight of the edge 27b indicating the number of occurrences between the node 16b and the node 18b is 1, and a value obtained by dividing this by 5 that is the number of the common event series on which the event series graph is based is 0.2. Therefore, the representative event series extraction unit 142 determines that the calculated value is smaller than the threshold value, and changes the skip flag to true. In this case, the representative event series extraction unit 142 uses 1 as the weight of the edge 27b indicating the number of occurrences of the node 16b that is “event (previous)” and the node 18b that is “event (back)” of the representative event series. Do not add to weights.
Here, since “event (after)” does not coincide with the node 18b that is the end point of the graph, the representative event series extraction unit 142 sets the node 16b that is “event (after)” to “event (before)”. Further, the node 18b which is the next event after “event (after)” is reset to a new “event (after)”, and the processing is continued.
Since the “event (after)” matches the node 18b which is the end point of the graph, the representative event sequence extraction unit 142 ends the determination of the simple road 10b, and determines the next simple road. At this time, the weights of the edges 21b, 22b, 23b, 24b, and 26b are added to the weight of the representative event sequence, and the total weight of the simple road 10b is 22. At this time, the representative event sequence extracted from the simple road 10b is “ABCD1”.
Next, an example in which determination is performed on the simple road 10c will be described. First, the representative event series extraction unit 142 sets the node 11c to “event (front)” and the node 12c to “event (back)”. Next, the representative event sequence extraction unit 142 sets the skip flag to false, the threshold value to 0.3, and initializes the weight of the representative event sequence to 0. The weight of the edge 21c indicating the number of occurrences between the node 11c and the node 12c is 5, and a value obtained by dividing this by 5 that is the number of common event sequences that are the basis of the event sequence graph is 1. Therefore, the representative event series extraction unit 142 determines that the calculated value is greater than or equal to the threshold value.
Since the skip flag is false, the representative event sequence extraction unit 142 does not select the node 11c as an element of the representative sequence and does not change the skip flag from false. Then, the representative event sequence extraction unit 142 selects the node 12c as an element of the representative event sequence, and adds the weight 5 of the edge 21c indicating the number of occurrences of the node 11c and the node 12c to the weight of the representative event sequence.
Subsequently, the representative event series extraction unit 142 repeats the same processing for the nodes 12c and 13c, the nodes 13c and 14c, the nodes 14c and 15c, and the nodes 15c and 16c. Then, the representative event series extraction unit 142 sets the node 16c to “event (front)” and the node 17c to “event (back)”. Next, the weight of the edge 28c indicating the number of occurrences between the node 16c and the node 17c is 1, and a value obtained by dividing the weight by 5 that is the number of common event sequences on which the event sequence graph is based is 0.2. Therefore, the representative event series extraction unit 142 determines that the calculated value is smaller than the threshold value, and changes the skip flag to true. In this case, the representative event series extraction unit 142 uses 1 as the weight of the edge 28c indicating the number of occurrences of the node 16c that is “event (previous)” and the node 17c that is “event (back)” of the representative event series. Do not add to weights.
Here, since “event (after)” does not coincide with the node 18c which is the end point of the graph, the representative event series extraction unit 142 sets the node 17c which is “event (after)” to “event (before)”. Further, the node 18c, which is the next event after “event (after)”, is reset to a new “event (after)”, and the processing is continued.
Then, the weight of the edge 29c indicating the number of occurrences between the node 17c which is “event (before)” and the node 18c which is “event (after)” is 1, and this is the basis of the event series graph. Since the value divided by 5 that is the number of common event sequences that are included is 0.2, the representative event sequence extraction unit 142 determines that the calculated value is smaller than the threshold value, and does not change the skip flag from true. In this case, the representative event sequence extraction unit 142 uses 1 as the weight of the edge 29c indicating the number of occurrences of the node 17c that is “event (previous)” and the node 18c that is “event (previous)” of the representative event sequence. Do not add to weights.
Since the “event (after)” matches the node 18c which is the end point of the graph, the representative event sequence extraction unit 142 ends the determination of the simple road 10c. At this time, the weights of the edges 21c, 22c, 23c, 24c, and 26c are added to the weight of the representative event sequence, and the total weight of the simple road 10c is 22. At this time, the representative event sequence extracted from the simple road 10c is “ABCD1”.
The representative event sequence extraction unit 142 compares the sum of the weights of the representative event sequences extracted from the simple roads 10a, 10b, and 10c, and the total sum of the weights of the representative event sequences extracted from the simple road 10a is 23. It is obtained that the sum of the weights of the representative event series extracted from the roads 10b and 10c is 22. Thus, the representative event sequence extraction unit 142 selects the representative event sequence “ABCD” extracted from the simple road 10a having the largest weight sum as the representative event sequence of the cluster.
If the ratio of the length of the representative event sequence to the length of the longest common event sequence in the same cluster is smaller than a predetermined value, the representative event sequence extraction unit 142 represents all the common event sequences in the cluster. It may be an event series. For example, when the predetermined value is set to 0.5, the representative event sequence is “AB”, and the longest common event sequence “ABCDE” exists in the same cluster, the length of the longest common event sequence is Since the ratio of the length of the representative event series is 0.4, which is less than the predetermined value, all the common event series including the common event series “ABCDE” are set as the representative event series. That is, when the length of the representative event sequence with respect to the maximum length of the common event sequence is smaller than a predetermined value, it is determined that an event sequence representing the common event sequence of the cluster cannot be created, and the common event sequence is A representative event series.
As described above, according to the representative event series extraction unit 142, even when a variant of malware that performs a similar operation occurs by clustering the event series and using a common event as an element of the representative event series, When a feature representative of a feature common to communications is seen, the determination by the detection unit 150 can be performed with the same event series. In other words, according to the representative event sequence extraction unit 142, it is not necessary to prepare a large number of event sequences used for detection even in a situation where malware variants frequently occur. A wide variety of subspecies can be supported. Furthermore, the detection apparatus 100 uses only an event sequence that represents a common event sequence, thereby reducing the number of event sequences for which collation determination is performed, and reducing the processing time.
The event collation unit 143 collates the event sequence of the monitoring target NW analysis result (for sequence extraction) with the detection event sequence candidate, and determines how much each detection event sequence candidate can detect the host in the monitoring target NW. calculate. Specifically, the event matching unit 143 uses the event sequence of the monitoring target NW analysis result (for sequence extraction) generated by the sequence generation unit 130 and the detection event sequence candidate extracted by the representative event sequence extraction unit 142. Get as input. Then, the event collating unit 143 collates both event series, and calculates the number of hosts corresponding to the monitoring target NW analysis result (for series extraction) determined to match. Then, the event matching unit 143 outputs the calculated number of hosts to the candidate determination unit 144 as an output.
Here, a method for determining a match between the detection event sequence candidate and the sequence of the monitoring target NW in the event matching unit 143 will be described with reference to FIGS. 8 and 9. FIG. 8 is a diagram illustrating an example of the common event sequence and the representative event sequence according to the embodiment. FIG. 9 is a diagram illustrating an example of processing in the event matching unit according to the embodiment. Here, as shown in FIG. 8, a case where the representative event series extraction unit 142 extracts a representative event series “ABD” from three common event series will be described as an example. As described above, in the following description, the detection event sequence candidate indicates a representative event sequence extracted by the representative event sequence extraction unit 142.
Here, the event collation unit 143 matches the length of the longest common subsequence (LCS) of the monitoring target NW analysis result sequence and the detection event sequence candidate with respect to the length of the detection event sequence candidate. Assume that the threshold for determining that the rates match is, for example, 0.8. (A) of FIG. 9 is a sequence obtained by analyzing the monitoring target NW. First, as shown in FIG. 9 (b), if the matching rate with the monitoring target NW sequence is determined when the common event sequence is determined as a detection event sequence candidate, 3 / 4, ie 0.75. As described above, since the match rate for determining match is 0.8, the event matching unit 143 does not determine that the common event sequence matches the sequence of the monitoring target NW.
Next, as shown in FIG. 9C, when detection is performed using a detection event sequence candidate, the match rate with the monitored NW sequence is 3/3, that is, 1.0. The unit 143 determines that the representative event sequence matches the monitoring target NW. At this time, the pattern “ABD” itself is not characteristic of malware infection. If “1”, “2”, “3”, etc. are included between “B” and “D”, The determination using the representative event sequence of the event matching unit 143 leads to an erroneous determination.
In order to prevent the erroneous determination as described above, the event matching unit 143 recalculates the match rate based on the following formula.
First match rate = LCS length between detection event sequence candidate and monitoring target NW sequence / sequence length of detection event sequence candidate Second match rate = sequence length of detection event sequence candidate / detection event sequence candidate Length of the longest common event sequence in the cluster for which is selected Match rate = first match rate × second match rate
In the example of FIG. 9, since the first match rate is 3/3 and the second match rate is 3/4, the match rate is 3/4, that is, 0.75, and the event matching unit 143 detects the detection event. It is determined that the sequence candidate does not match the sequence of the monitoring target NW. Note that the determination method in the event matching unit 143 is not limited to the method described here, and a method of using the first match rate as the match rate without using the second match rate may be used.
Based on the number of detection hosts for each detection event sequence candidate calculated by the event matching unit 143, the candidate determination unit 144 detects the detection event sequence when the ratio of the number of detection hosts to the total number of hosts of the monitoring target NW is equal to or less than a certain value. Candidates are output as a detection event sequence. Specifically, the candidate determination unit 144 divides the number of detection hosts of the detection event sequence candidate determined by the event matching unit 143 to match the event sequence by the total number of hosts in the monitoring target NW, and The percentage of detected hosts is calculated. Then, the candidate determination unit 144 outputs, from the detection event sequence candidates, an event sequence having a detection host ratio equal to or less than a certain value as a detection event sequence. As a result, in the same way as the processing of the excluded event extraction unit 131, the candidate determination unit 144 is configured so that, in general, there are few terminals that are infected with malware in the monitoring target NW, leading to erroneous detection in advance from the detection event sequence. Can be excluded. For this reason, according to the process of the candidate determination part 144, it becomes possible to reduce the misdetection at the time of detecting an infected terminal in the monitoring object NW.
The detection unit 150 includes an event matching unit 151 and a detection result output unit 152, and detects a malware-infected terminal in the monitoring target NW. Specifically, the event collation unit 151 collates whether the monitoring target NW analysis result (for detection) matches the event series of the detection event series. Then, the detection result output unit 152 outputs host information that is determined to match the detection event sequence as a result of the collation by the event collation unit 151. In other words, the detection result output unit 152 determines that the host that is determined to match the detection event sequence that is the signature is likely to be a malware-infected terminal, and is determined to match the detection event sequence. Malware-infected terminals are detected by outputting information that can identify the host to be detected. Note that the event matching unit 151 may use the same determination method as the event matching unit 143. In that case, the detection event sequence candidate in the event matching unit 143 is replaced with the detection event sequence in the event matching unit 151.
Next, the procedure of the detection process performed by the above-described detection apparatus 100 will be described in detail.
(Exclusion event extraction process)
First, the exclusion event extraction process executed by the exclusion event extraction unit 131 will be described with reference to FIG. FIG. 10 is a flowchart illustrating an excluded event extraction processing procedure by the excluded event extraction unit 131 according to the embodiment.
As illustrated in FIG. 10, the excluded event extraction unit 131 reads the monitoring target NW analysis result (for series extraction) as an input (step S101). Then, the excluded event extraction unit 131 acquires the number of hosts in the monitoring target NW (step S102). Here, if the number of hosts existing in the monitoring target NW is known in advance, the number of hosts of the monitoring target NW may be the number, or the number of hosts appearing in the monitoring target NW analysis result (for series extraction) may be monitored. It may be regarded as the number of hosts of the target NW. In other words, the number of hosts in the monitoring target NW is the total number of hosts that can be observed in the monitoring target NW, and when the total number of hosts existing in advance is observed, the total number of hosts is applied and the total number of hosts When the number is unknown, the total number of hosts that can be observed based on the monitoring target NW analysis result (for sequence extraction) is applied.
Subsequently, the excluded event extraction unit 131 determines whether or not the process of determining whether to exclude all events included in the read monitoring target NW analysis result (for series extraction) has been performed ( Step S103). When it is determined that the process has been executed for all events, the excluded event extraction process ends (Yes in step S103).
On the other hand, if it is determined that the process has not been executed for all events (No at Step S103), the excluded event extracting unit 131 continues the excluded event extracting process. At this time, the excluded event extraction unit 131 divides the number of hosts detected in a certain event by the number of hosts of the monitoring target NW, and acquires the event detection rate (step S104).
Then, the excluded event extraction unit 131 determines whether or not the detection ratio is greater than a value specified in advance (step S105). When it is determined that the detection ratio is larger than the value specified in advance (Yes at Step S105), the excluded event extracting unit 131 sets the event to be determined as an excluded event (Step S106). On the other hand, when it is determined that the detection ratio is not greater than the value specified in advance (No in step S105), the excluded event extracting unit 131 does not set the event as an excluded event and continues processing for different events. (Transition to step S103).
Thus, the excluded event extraction unit 131 determines that an event confirmed by many hosts does not capture only the characteristics of communication by malware, extracts the event, and sets it as an excluded event. As a result, the excluded event extraction unit 131 can reduce false detection in the detection process of the infected terminal.
(Event series generation processing)
Next, event sequence generation processing executed by the event sequence generation unit 132 will be described with reference to FIG. FIG. 11 is a flowchart showing an event sequence generation processing procedure by the event sequence generation unit 132.
As shown in FIG. 11, the event sequence generation unit 132 processes all host or malware analysis results for event sequence generation processing of the monitoring target NW analysis results (for sequence extraction and detection) and malware communication analysis results. It is determined whether or not the process has been completed (step S201). When it is determined that the process has been executed for all, the event series generation process ends (Yes in step S201).
On the other hand, when it is determined that the analysis results of all the hosts or malware have not been processed (No at Step S201), the event series generation unit 132 specifies a host or malware that reads the analysis results (Step S202). The event series generation unit 132 generates an event series for each host in the monitoring target NW when extracting the event series from the monitoring target NW analysis result. Here, for host identification, for example, the IP address of the host is used. Further, the event sequence generation unit 132 generates an event sequence for each malware when generating the event sequence from the malware communication analysis result. Here, for example, a malware hash value is used for malware identification. It is assumed that both the monitoring target NW analysis result and the malware communication analysis result are sorted by the time when the event is confirmed.
Then, prior to the process described below, the event series generation unit 132 initializes the immediately preceding event time and the event series (during processing) (step S203).
First, the event series generation unit 132 determines whether or not the analysis result of the designated host or malware has been processed (step S204). When it is determined that the analysis result has been processed (Yes in step S204), the event sequence generation unit 132 determines whether there is an event sequence that is not output as an event sequence (that is, an event sequence that is being generated). (Step S205). If there is an event series that has not been output as an event series (Yes at Step S205), the event series generation unit 132 outputs the event series being processed as an event series (Step S206).
On the other hand, if there is no event series being processed that has not been output as an event series (No at step S205), the event series generation unit 132 shifts the process to step S201.
If it is determined in step S204 that the analysis result has not been processed (No in step S204), the event sequence generation unit 132 reads the specified host or malware event and event occurrence time (step S207). Then, the event series generation unit 132 determines whether or not the read event corresponds to an exclusion event (step S208). When it corresponds to the excluded event (Yes at Step S208), the event series generation unit 132 shifts the process to Step S204 without adding the read event to the event series. Although the detection error rate can be reduced by extracting and excluding the excluded event, the event extracting and excluding process may not be performed in the event series generation process.
On the other hand, when the read event does not correspond to the excluded event (No at Step S208), the event series generation unit 132 records the event occurrence time of the read event (Step S209). Then, the event series generation unit 132 determines whether the recorded event occurrence time is separated from the previous event time by a certain time or more (step S210).
If the event occurrence time is a predetermined time or more away from the previous event time (Yes at step S210), the read event is added to an event sequence different from the event sequence being processed, and therefore the event sequence generation unit 132 Outputs the event sequence being processed as an event sequence (step S211). In this case, the event series generation unit 132 initializes the output event series (during processing) (step S212).
In step S210, when the event occurrence time is not apart from the previous event time by a certain time or longer (No in step S210), the event series generation unit 132 sets the event occurrence time of the read event as the previous event time (step S213). . In other words, when the event occurrence time is not separated from the previous event time by a certain time or more, the event sequence generation unit 132 estimates that the event is an element of the same event sequence as the previous event, and the event sequence (processing It is determined whether or not to be added (step S214 described later).
Then, the event sequence generation unit 132 determines whether or not the read event is included in the event sequence (during processing) (step S214). If the read event is included in the event sequence (processing is in progress) (Yes at step S214), the event sequence generation unit 132 does not add a duplicate event to the event sequence (processing), so the process proceeds to step S204. Let
On the other hand, when the read event is not included in the event series (processing) (No in step S214), the event series generation unit 132 adds the event to the event series (processing) (step S215). Thereafter, the event series generation unit 132 shifts the processing to step S204.
As described above, when the read event corresponds to the excluded event, the event sequence generation unit 132 does not incorporate the event into the event sequence. In addition, the event series generation unit 132 records the time at which the event occurred, compares the time at which the event occurred with the time at which the previous event occurred, and determines whether or not a certain time or more is left. Thereby, the event sequence generation unit 132 generates an event sequence so that one event sequence is formed by events having a short occurrence interval between events. Furthermore, the event series generation unit 132 determines whether or not an event to be processed is included in the event series (during processing), and if it is included, the event is not added to the event series. That is, there are no overlapping events in the generated event series. Note that the determination of whether or not to add an overlapping event to the event series (during processing) (step S214) may not be performed based on the characteristics of the monitoring target NW analysis result and the malware communication analysis result. For example, if the number of events confirmed in the monitoring target NW analysis result and the malware communication analysis result is small (for example, in the case of only one type, etc.), are duplicate events added to the event sequence (during processing)? It is possible to add all the events that do not correspond to the excluded event to the event series (during processing) without determining whether or not they are not.
(Common event series extraction process)
Next, common event sequence extraction processing executed by the common event sequence extraction unit 141 will be described with reference to FIG. FIG. 12 is a flowchart showing a common event sequence extraction processing procedure by the common event sequence extraction unit 141.
As illustrated in FIG. 12, the common event sequence extraction unit 141 reads an event sequence extracted from the malware communication analysis result as a processing target (step S301). Then, the common event sequence extraction unit 141 generates a similarity matrix between event sequences and performs hierarchical clustering (step S302). Here, in the generation of the similarity matrix, for example, a uniquely identifiable character is assigned to each event, the event sequence is regarded as a character string, the Levenstein distance between the event sequences is calculated, and the event sequence is calculated. Find the similarity of.
Then, in the hierarchical clustering to be performed, the common event series extraction unit 141 sets event series having a similarity equal to or higher than a preset similarity cluster (step S303).
Here, the common event sequence extraction unit 141 determines whether or not the process of extracting the common event sequence from all the clusters has been executed (step S304). When it is determined that the process of extracting the common event series from all the clusters has been executed (Yes in step S304), the common event series extraction process by the common event series extraction unit 141 ends.
On the other hand, when it is determined that the process of extracting the common event series from all the clusters has not been executed (No at Step S304), the common event series extracting unit 141 specifies the cluster from which the common event series is extracted (Step S305). .
Then, the common event sequence extraction unit 141 extracts the longest common partial sequence from the partial sequences common to the event sequences in the same cluster (step S306). Then, the common event sequence extraction unit 141 outputs the longest common partial sequence longer than a predetermined length as a common event sequence (step S307).
As described above, the common event sequence extraction unit 141 performs clustering after calculating the similarity between the event sequences extracted from the malware communication analysis result. Thereafter, the common event sequence extraction unit 141 extracts event sequences that are commonly confirmed among the event sequences among event sequences having a certain degree of similarity or more, and sets them as common event sequences. When only a single event sequence exists in the same cluster, the common event sequence extraction unit 141 sets the event sequence as a common event sequence if the length of the event sequence is equal to or greater than a certain length. Output. Further, the common event sequence extraction unit 141 can arbitrarily set the length of an event sequence that is a common event sequence. For example, the common event sequence extraction unit 141 may set a case where two or more events are included in the sequence as the length of the minimum event sequence.
(Representative event series extraction process)
Next, a representative event sequence extraction process executed by the representative event sequence extraction unit 142 will be described with reference to FIG. FIG. 13 is a flowchart illustrating a representative event sequence extraction processing procedure by the representative event sequence extraction unit.
As illustrated in FIG. 13, the representative event sequence extraction unit 142 determines whether a representative event sequence has been extracted from all clusters (step S <b> 401). If it is determined that representative event sequences have been extracted from all clusters, the representative event sequence extraction process ends (Yes at step S401).
On the other hand, when it is determined that the representative event series has not been extracted from all the clusters (No at Step S401), the representative event series extracting unit 142 designates a cluster from which the representative event series is extracted (Step S402). Then, the representative event series extraction unit 142 generates an event series graph from the common event series of the designated cluster (step S403). Further, the representative event sequence extraction unit 142 extracts a simple road from the start point to the end point of the event sequence graph (step S404).
Next, the representative event sequence extraction unit 142 determines whether all simple roads have been determined (step S405). If the representative event sequence extracting unit 142 determines that all simple roads have been determined (Yes in step S405), the representative event sequence having the largest weight is output as the representative event sequence of the cluster. (Step S406). If the representative event series extraction unit 142 determines that all simple roads have not been determined (No in step S405), the representative event series extraction unit 142 selects a simple road to be identified (step S407).
When the representative event series extraction unit 142 selects a path to be identified, the first event of the graph is set to “event (previous)”, the next event is set to “event (post)”, and the skip flag is false. And the weight of the representative event sequence is initialized to 0 (step S408). Then, the representative event series extraction unit 142 determines whether or not the occurrence frequency is greater than or equal to the threshold value between “event (before)” and “event (after)” (step S409). Here, the occurrence frequency is a value obtained by dividing the number of times that the relationship between a certain “event (before)” and “event (after)” appears in the event series graph by the number of common event series forming the event series graph. is there. Further, when it is determined that the occurrence frequency is greater than or equal to the threshold value between “event (previous)” and “event (rear)”, the representative event series extraction unit 142 determines that the skip flag is true. Is determined (step S410). If it is determined that the skip flag is true (Yes at step S410), the representative event sequence extraction unit 142 selects “event (previous)” as an element of the representative event sequence and sets the skip flag to false. (Step S411).
Next, the representative event series extraction unit 142 selects “event (after)” as an element of the representative event series, and calculates the number of occurrences between “event (before)” and “event (after)” in the representative event series. It adds to a weight (step S412). If it is determined that the occurrence frequency is not equal to or greater than the threshold between “event (before)” and “event (after)” (No in step S409), the representative event sequence extraction unit 142 sets the skip flag to true. (Step S413).
If “event (after)” is not the end point of the graph (No in step S414), the representative event series extraction unit 142 sets “event (after)” to “event (before)” and “event (after) The event after “after” is set to “event (after)” (step S415). If “event (after)” is the end point of the graph (Yes at step S414), the representative event sequence extraction unit 142 determines whether all paths have been determined (step S405). The representative event series finally output by the representative event series extraction unit 142 is set as a detection event series candidate in subsequent processing.
As described above, according to the representative event series extraction unit 142, even when a variant of malware that performs a similar operation occurs by clustering the event series and using a common event as an element of the representative event series, When a feature representative of a feature common to communications is seen, the determination by the detection unit 150 can be performed with the same event series. In other words, according to the representative event sequence extraction unit 142, it is not necessary to prepare a large number of event sequences used for detection even in a situation where malware variants frequently occur. A wide variety of subspecies can be supported. Furthermore, the detection apparatus 100 uses only a representative event sequence, thereby reducing the number of event sequences for which collation determination is performed, and reducing the processing time.
(Candidate judgment processing)
Next, the candidate determination process executed by the event matching unit 143 and the candidate determination unit 144 will be described with reference to FIG. FIG. 14 is a flowchart illustrating a candidate determination processing procedure performed by the event matching unit 143 and the candidate determination unit 144.
As illustrated in FIG. 14, the event matching unit 143 acquires an event sequence of the monitoring target NW analysis result (for sequence extraction) as a detection target event sequence (step S501). In addition, the event matching unit 143 acquires the detection event sequence candidate extracted by the common event sequence extraction unit 141 as a signature sequence (step S502).
Then, the event matching unit 143 performs an event matching process on the acquired detection target event series and signature series (step S503). Note that the event matching process by the event matching unit 143 is the same as the event matching process according to the detection unit 150, and details will be described later.
Subsequently, the candidate determination unit 144 divides the number of detected hosts for each verification event sequence determined to be matched by the event verification processing by the number of hosts of the monitoring target NW, and calculates the detected host ratio for each verification event sequence. Calculate (step S504). Here, the collation event series refers to one event series selected from the signature series. That is, the candidate determination unit 144 calculates the detected host ratio for each event series included in the signature series. Then, the candidate determination unit 144 outputs a matching event sequence having a detection host ratio of a certain value or less as a detection event sequence (step S505). Thereby, the candidate determination process which the event collation part 143 and the candidate determination part 144 perform is complete | finished.
In this way, the candidate determination unit 144 is based on the number of detected hosts for each detection event sequence candidate collated by the event collation unit 143, and the ratio of the number of detected hosts to the total number of hosts of the monitoring target NW is equal to or less than a certain value. In addition, the detection event sequence candidate is output as a detection event sequence. This is similar to the processing of the excluded event extraction unit 131. In general, based on the fact that there are few terminals that are infected with malware in the monitoring target NW, processing for excluding those that lead to false detection from the detection event series in advance. It is.
In other words, assuming that there are few terminals infected with malware in the monitored NW, the event series candidates for detection determined to be matched in this process are not only malware communications but also event series that can be confirmed by general communications. Therefore, when used for detection, it can be determined that the event series is likely to induce false detection. For this reason, it is possible to reduce the false detection in the detection part by excluding beforehand the event sequence of the communication of the malware which is difficult to distinguish from general communication by the process of the candidate determination part 144. For example, the detection apparatus 100 determines that the result of dividing the number of detected hosts for each verification event sequence determined to be matched by the event verification process by the number of hosts of the monitoring target NW is 0, that is, the detection event sequence candidate is the monitoring target NW. Only the event series that did not detect the event series of the analysis result (for series extraction) may be output as the detection event series. As a result, the detection apparatus 100 can suppress the detection event sequence from being mixed with something that may cause a false detection.
Next, detection processing executed by the detection unit 150 will be described with reference to FIG. FIG. 15 is a flowchart illustrating a detection processing procedure performed by the detection unit 150.
As illustrated in FIG. 15, the event matching unit 151 associated with the detection unit 150 acquires the event sequence of the monitoring target NW analysis result (for detection) as the detection target event sequence (step S601). In addition, the event matching unit 151 acquires the detection event sequence extracted by the detection sequence extraction unit 140 as a signature sequence (step S602). Then, the event matching unit 151 executes an event matching process for the acquired detection target event series and signature series (step S603).
Subsequently, the detection result output unit 152 related to the detection unit 150 determines that the host determined to be matched by the event matching process is a malware-infected host, and outputs the result as a detection result (step S604). Thereby, the detection process which the detection part 150 performs is complete | finished.
In this manner, the detection unit 150 collates the event sequence of the monitoring target NW analysis result (for detection) generated by the sequence generation unit 130 with the detection event sequence extracted by the detection sequence extraction unit 140. Thereby, since the detection part 150 can collate the event series from which the event which can be observed in the monitoring object NW beforehand, or the time series of the event was excluded, it detects the communication normally generated in the monitoring object NW by mistake. This can reduce the situation and detect malware-infected terminals.
Next, referring to FIG. 16, the matching process executed by the event matching unit 151 related to the detection unit 150 will be described. FIG. 16 is a flowchart showing a verification processing procedure by the event verification unit 151. Note that the event matching unit 143 related to the detection sequence extraction unit 140 also performs the same processing as the processing described below.
As illustrated in FIG. 16, the event matching unit 151 acquires the event series of the monitoring target NW analysis result (for detection) as the detection target event series (step S701). In addition, the event matching unit 151 acquires the detection event sequence extracted by the detection sequence extraction unit 140 as a signature sequence (step S702).
Then, the event matching unit 151 determines whether all the detection target event sequences have been determined (step S703). When all the detection target event sequences have been determined (Yes at Step S703), the matching process by the event matching unit 151 ends.
On the other hand, when all the detection target event sequences have not been determined (No at Step S703), the event matching unit 151 acquires the determination target event sequence and the host information from the detection target event sequence (Step S704). Then, based on the acquired host information, the event matching unit 151 determines whether or not all the signature sequences have been determined for the host to be detected (step S705). When the determination is made for all signature series (Yes at step S705), the event matching unit 151 shifts the processing to step S703.
On the other hand, when the determination is not made with all signature series (No at step S705), the event matching unit 151 acquires a matching event series from the signature series (step S706). Then, the event matching unit 151 acquires the longest common partial sequence length of the determination target event series and the matching event series (step S707).
Subsequently, the event matching unit 151 determines whether or not a value obtained by dividing the longest common partial sequence length by the matching event sequence length is larger than a value specified in advance (step S708). When the value is larger than the value specified in advance (Yes at Step S708), the event matching unit 151 determines that the determination target event sequence matches the matching event sequence (Step S709).
On the other hand, when the value is not larger than the value specified in advance (No at Step S708), the event matching unit 151 determines that the determination target event series does not match the matching event series (Step S710).
Then, the event matching unit 151 outputs the matching event sequence, the host information of the determination target event sequence, and the determination result (step S711). Then, the event matching unit 151 shifts the process to step S705.
As described above, the event matching unit 151 executes a matching process with the event sequence to be detected using the event sequence extracted based on the characteristic communication of the malware as a signature sequence. Thereby, the detection apparatus 100 can detect a terminal infected with malware having a similar communication pattern with few false detections.
In addition, the process similar to embodiment may be implement | achieved by the detection system provided with the terminal device in the monitoring object NW, and the detection apparatus 100. FIG. In this case, the terminal device generates a predetermined event in the monitoring target NW, and the detection device 100 acquires the event for each terminal device. In addition, the malware-infected terminal detection system may include an information processing device that virtually generates malware communication. In this case, the detection device 100 included in the detection system acquires an event generated by the information processing device as a malware analysis result.
As described above, the detection apparatus 100 according to the embodiment is an event that is an event that matches a rule characterizing communication among the communication of the monitoring target NW and the communication generated by the malware, From an event acquired for each identifier for distinguishing malware, an event sequence formed based on a time series is generated based on the occurrence order of events. Then, the detection apparatus 100 calculates a similarity between event sequences based on communication generated by malware, sets event sequences having a certain degree of similarity or higher in the same cluster, and is common among event sequences belonging to the same cluster. Events that appear regularly are extracted, and an event sequence of a certain length or more obtained by combining the extracted events in chronological order is extracted as a common event sequence. Furthermore, the detection apparatus 100 extracts a representative event sequence including a relationship between events having a high appearance frequency as a detection event sequence from common event sequences that are similar among a plurality of common event sequences. Then, the detection apparatus 100 is the ratio of the length of the matching portion between the determination target event series that is an event series based on the communication of the monitoring target NW and the detection event series to the length of the detection event series. When it is determined that the event series match according to the match rate, it is detected that a malware-infected terminal exists in the monitoring target NW.
Thereby, the detection apparatus 100 according to the embodiment can reduce signatures that are patterns to be collated with the monitoring target NW, and can reduce the time required for collation. Further, the detection apparatus 100 does not use a single malware communication as a signature, but detects a set of event sequences representing a common event sequence among event sequences clustered based on the malware communication analysis result. A series (signature). Thereby, the detection apparatus 100 can detect not only known malware but also variants of malware that perform communication similar to known malware.
In addition, the detection apparatus 100 generates a directed graph from the common event series, in which the event is a node, the generation order between events is an edge, and the number of appearances of the event context is the edge weight. Then, the detection apparatus 100 calculates the sum of the weights for each simple road of the directed graph, and sets the simple road showing the maximum weight as the representative event series. Thereby, the detection apparatus 100 can extract a representative event sequence that can be detected most efficiently from similar common event sequences.
In addition, the detection apparatus 100 calculates the sum of the weights of the edges included in the simple road of the directed graph and whose weight is equal to or greater than a predetermined threshold, and sets the simple road having the maximum weight sum as the representative event sequence. . As a result, the detection apparatus 100 can reduce the number of events that are subject to representative event series extraction in advance, and thus can reduce processing.
In addition, when the ratio of the length of the representative event sequence to the length of the longest common event sequence in the same cluster is smaller than a predetermined value, the detection apparatus 100 sets the common event sequence in the same cluster as the representative event sequence. As a result, when it is difficult to extract an event sequence representing a cluster, it is possible to prevent an extremely short representative event sequence from being generated, and to reduce erroneous detection.
In addition, the detection apparatus 100 is the first ratio that is the ratio of the length of the matching portion between the determination target event sequence that is an event sequence based on the communication of the monitoring target network and the detection event sequence to the length of the detection event sequence. The value obtained by multiplying the match rate by the second match rate, which is the ratio of the length of the detection event sequence to the length of the longest common event sequence in the cluster to which the detection event sequence belongs, is equal to or greater than a predetermined threshold value. If there is, it is determined that the determination target event sequence matches the detection event sequence, and it is detected that a malware infected terminal exists in the monitoring target network. Thereby, false detection can be reduced.
(Configuration etc.)
Note that each component of each illustrated apparatus is functionally conceptual and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. Further, all or any part of each processing function performed in each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic.
In addition, among the processes described in the present embodiment, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above-described document and drawings can be arbitrarily changed unless otherwise specified.
In addition, it is possible to create a program in which processing executed by the detection apparatus 100 according to the above-described embodiment is described in a language that can be executed by a computer. In this case, the same effect as the above-described embodiment can be obtained by the computer executing the program. Further, such a program may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read by the computer and executed to execute the same processing as in the above embodiment. Hereinafter, an example of a computer that executes a detection program that realizes the same function as that of the detection apparatus 100 will be described.
FIG. 17 is a diagram illustrating a computer that executes a malware-infected terminal detection program. As shown in FIG. 17, a computer 1000 includes, for example, a memory 1010, a CPU (Central Processing Unit) 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network. Interface 1070. These units are connected by a bus 1080.
The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM (Random Access Memory) 1012. The ROM 1011 stores a boot program such as BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1090. The disk drive interface 1040 is connected to the disk drive 1041. For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1041. For example, a mouse 1110 and a keyboard 1120 are connected to the serial port interface 1050. For example, a display 1130 is connected to the video adapter 1060.
Here, as shown in FIG. 17, the hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. Each piece of information described in the above embodiment is stored in, for example, the hard disk drive 1090 or the memory 1010.
Further, the detection program is stored in the hard disk drive 1090 as a program module in which a command executed by the computer 1000 is described, for example. Specifically, a program module describing each process executed by the detection apparatus 100 described in the above embodiment is stored in the hard disk drive 1090.
Data used for information processing by the detection program is stored as program data in, for example, the hard disk drive 1090. Then, the CPU 1020 reads out the program module 1093 and the program data 1094 stored in the hard disk drive 1090 to the RAM 1012 as necessary, and executes the above-described procedures.
The program module 1093 and the program data 1094 related to the detection program are not limited to being stored in the hard disk drive 1090. For example, the program module 1093 and the program data 1094 are stored in a removable storage medium and read by the CPU 1020 via the disk drive 1041 or the like. May be. Alternatively, the program module 1093 and the program data 1094 related to the detection program are stored in another computer connected via a network such as a LAN (Local Area Network) or a WAN (Wide Area Network), and are transmitted via the network interface 1070. It may be read by the CPU 1020.
DESCRIPTION OF SYMBOLS 100 Detection apparatus 130 Sequence generation part 131 Excluded event extraction part 132 Event series generation part 140 Detection series extraction part 141 Common event series extraction part 142 Representative event series extraction part 143 Event collation part 144 Candidate determination part 150 Detection part 151 Event collation part 152 Detection result output unit
Of the communications of the monitored network and the communications generated by malware, the event is an event that matches the rules that characterize the communications, and is obtained from the event acquired for each identifier that distinguishes the terminal or malware of the monitored network. A sequence generation unit that generates an event sequence formed based on the occurrence order of
In a cluster formed by event sequences with a certain degree of similarity between event sequences based on communication generated by malware, events that appear in common among event sequences belonging to the same cluster are extracted, and the extracted events are An event sequence of a certain length or more combined in sequence order is extracted as a common event sequence, and a representative event sequence consisting of relationships between frequently occurring events is extracted from the common event sequence similar to a plurality of the common event sequences. A detection sequence extraction unit for extracting as a detection event sequence;
When it is determined that the event sequence based on the communication of the monitoring target network generated by the sequence generation unit matches the detection event sequence extracted by the detection sequence extraction unit, the monitoring target A detection unit that detects the presence of malware-infected terminals on the network;
A device for detecting a malware-infected terminal, comprising:
The detection sequence extraction unit includes:
From the common event series, a directed graph is generated in which the event is a node, the occurrence order between the events is an edge, and the number of appearances of the context of the event is the weight of the edge, and for each simple path of the directed graph, the weight of the weight 2. The apparatus for detecting a malware-infected terminal according to claim 1, wherein a sum total is calculated, and the simple road having the maximum sum of the weights is set as the representative event series.
Of the edges included in the simple road of the directed graph, the sum of the weights is calculated for an edge whose weight is equal to or greater than a predetermined threshold, and the simple road having the maximum sum of the weights is defined as the representative event sequence. The apparatus for detecting a malware-infected terminal according to claim 2, wherein:
When the ratio of the length of the representative event sequence to the length of the longest common event sequence in the same cluster is smaller than a predetermined value, all the common event sequences included in the same cluster are represented by the representative event sequence. The apparatus for detecting a malware-infected terminal according to claim 1, wherein:
A first match rate that is a ratio of a length of a match portion between a determination target event sequence that is an event sequence based on communication of the monitored network and the detection event sequence to a length of the detection event sequence; A value obtained by multiplying the length of the detection event sequence by a second match rate, which is a ratio of the length of the longest common event sequence in the cluster to which the detection event sequence belongs, is equal to or greater than a predetermined threshold. The method according to claim 1, wherein the determination target event sequence and the detection event sequence are determined to match, and it is detected that a malware-infected terminal exists in the monitoring target network. Malware infected terminal detection device.
Malware execution environment,
A monitored network,
A malware-infected terminal detection system comprising a malware-infected terminal detection device,
The malware-infected terminal detection device is:
For each identifier that distinguishes a terminal or malware in the monitored network, which is an event that matches a rule characterizing the communication among communications in the monitored network and communications generated by malware executed in the malware execution environment A sequence generation unit that generates an event sequence formed based on the occurrence order of the event from the event acquired in
A malware-infected terminal detection system characterized by comprising:
A malware-infected terminal detection method executed by a malware-infected terminal detection system having a malware-infected terminal detection device,
For each identifier that distinguishes a terminal or malware in the monitored network, which is an event that matches a rule characterizing the communication among communications in the monitored network and communications generated by malware executed in the malware execution environment A sequence generation process for generating an event sequence formed based on the occurrence order of the event from the event acquired in
In a cluster formed by event sequences with a certain degree of similarity between event sequences based on communication generated by malware, events that appear in common among event sequences belonging to the same cluster are extracted, and the extracted events are An event sequence of a certain length or more combined in sequence order is extracted as a common event sequence, and a representative event sequence consisting of relationships between frequently occurring events is extracted from the common event sequence similar to a plurality of the common event sequences. A detection sequence extraction step for extracting as a detection event sequence;
When it is determined that the event sequence based on the communication of the monitoring target network generated by the sequence generation step matches the detection event sequence extracted by the detection sequence extraction step, the monitoring target A detection process for detecting the presence of malware-infected terminals in the network;
A method for detecting a malware-infected terminal, characterized by comprising:
Of the communications of the monitored network and the communications generated by malware, the event is an event that matches the rules that characterize the communications, and is obtained from the event acquired for each identifier that distinguishes the terminal or malware of the monitored network. A sequence generation step for generating an event sequence formed based on the occurrence order of
If it is determined that the event sequence based on the communication of the monitoring target network generated by the sequence generation step matches the detection event sequence extracted by the detection sequence extraction step, the monitoring target A detection program for detecting a malware-infected terminal for causing a computer to execute a detection step for detecting the presence of a malware-infected terminal on a network.
JP2017506466A 2015-03-18 2016-03-08 Malware-infected terminal detection device, malware-infected terminal detection system, malware-infected terminal detection method, and malware-infected terminal detection program Active JP6348656B2 (en)
JPWO2016147944A1 true JPWO2016147944A1 (en) 2017-08-31
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JP2017506466A Active JP6348656B2 (en) 2015-03-18 2016-03-08 Malware-infected terminal detection device, malware-infected terminal detection system, malware-infected terminal detection method, and malware-infected terminal detection program
WO2016076334A1 (en) * 2014-11-14 2016-05-19 日本電信電話株式会社 Device for detecting terminal infected by malware, method for detecting terminal infected by malware, and program for detecting terminal infected by malware
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