Abstract:
A system is used for detection of advanced persistent and non-persistent threats in a computerized environment. The system is connected to a plurality of user devices coupled to an enterprise&#39;s network. The system receives via an interface an electronic notification of at least one event in the operating system of the computer. The system then analyzes the at least one event. The system then generates a causality chain for the at least one event respective of the analysis. The causality chain comprises all the threads that attributed to the at least one event in a chronological order. The system then identifies a main thread that started the causality chain that led to the at least one event. Then, the system determines whether the main thread is associated with malicious software. Upon determination that the main thread is associated with malicious software, the causality chain is marked as infected.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This Application claims the benefit of co-pending U.S. Provisional Application Ser. No. 62/274,799 filed Jan. 5, 2016, the entire contents of which are incorporated herein by reference in their entirety. 
     
    
     BACKGROUND 
       [0002]    1. Field 
         [0003]    The disclosure generally relates to data security, and more specifically, to a system and methods for causality identification and attributions determination of processes in a network. 
         [0004]    2. Description of Related Art 
         [0005]    The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, issues identified with respect to one or more approaches should not assume to have been recognized in any prior art on the basis of this section, unless otherwise indicated. 
         [0006]    As organizations and enterprises get bigger, they are more and more exposed to malicious attacks. Kaspersky® reports detections of over 300,000 different malware variants in a single day. The United Kingdom government reports over 100,000 cyber-attacks on British companies every day. 
         [0007]    In order to identify such attacks, a number of different anti-virus applications are currently available. Such anti-virus applications force security teams of large enterprises to manage thousands of new alerts every day, when responding to a single alert may take days, weeks and sometimes months. 
         [0008]    These applications must be deployed into a computerized environment and attempt to identify malicious activity within the network. Other solutions may also be known. For example, anti-virus solutions that detect and remove known viruses by identifying “signatures” of such viruses may be available. 
         [0009]    The majority of these solutions rely upon a basic engine that searches suspect files for the presence of predetermined virus signatures. However, these related art solutions for identifying security incidents are not effective enough and malicious activity may go undetected. 
         [0010]    In the view of the shortcoming of related art, it would be advantageous to provide an efficient solution for detecting security incidents in a computerized environment by automatically validating security alerts. It would further be advantageous if such solution shall further enable a real-time assessment of damages resulted from a security incident. 
       SUMMARY 
       [0011]    Exemplary implementation of the disclosed teachings overcome the above disadvantages and other disadvantages not described above. Also, an exemplary implementation of the disclosed teachings is not required to overcome the disadvantages described above, and an exemplary implementation of the disclosed teachings of the present inventive concept may not overcome any of the problems described above. 
         [0012]    In some exemplary implementation of the disclosed teachings, a computerized method for causality identification and attributions determination of processes in a network, includes receiving a request to identify one or more process initiated in an enterprise&#39;s system, and identifying one or more process initiated in an the enterprise&#39;s system. Additionally, the computerized method includes analyzing one or more process, and determining whether the type of the process is a) a major system process, b) a minor system process or c) a non-system process. Finally, the computerized method includes continuously monitoring one or more process, and generating analytics for the processes. 
         [0013]    In other exemplary implementation of the disclosed teachings, an apparatus for causality identification and attributions determination of processes in a network, includes an interface to an enterprises&#39; system, at least one non-transitory memory configured to store instructions, and at least one processor configured to read the instructions and operate as instructed by the instructions. The instructions include receive instructions that receive a request to identify at least one process initiated in an enterprise&#39;s system, mining instructions that identify the at least one process initiated in the enterprise&#39;s system, analyze instructions that analyze the at least one process, determination instructions that determine a type of the at least one process, where the type could be a) major system process, b) a minor system process or c) a non-system process, monitor instructions that continuously monitor the at least one process, and generate instructions that generate analytics respective of the at least one process. 
         [0014]    In yet other exemplary implementation of the disclosed teachings, a non-transitory computer readable storage medium, implemented by at least one processor allows a computer to receive a request to identify at least one process initiated in an enterprise&#39;s system, and identify the at least one process initiated in the enterprise&#39;s system. Additionally, the non-transitory computer readable storage medium allows a computer to determine a type of the at least one process, where the type could be a) major system process, b) a minor system process or c) a non-system process, analyze the at least one process, and continuously monitor the at least one process, generate analytics respective of the at least one process. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]    The foregoing and other objects, features, and advantages of the disclosed teachings will be apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: 
           [0016]      FIG. 1 —is a block diagram of a network system used to describe the operation of the system according to an exemplary implementation of the disclosed teachings. 
           [0017]      FIG. 2 —is a schematic diagram of an apparatus used to describe the operation of the system according to an exemplary implementation of the disclosed teachings. 
           [0018]      FIG. 3 —is a flowchart describing a method for causality identification and attributions determination of processes in a network according to an exemplary implementation of the disclosed teachings. 
           [0019]      FIG. 4 —is a flowchart describing a method for identifying a process type in a network according to an exemplary implementation of the disclosed teachings. 
           [0020]      FIG. 5 —is a flowchart describing a method for identifying security incidents respective of processes&#39; type according to an exemplary implementation of the disclosed teachings. 
           [0021]      FIG. 6 —is a schematic diagram of a determination of processes type according to an exemplary implementation of the disclosed teachings. 
           [0022]      FIG. 7 —is a simulation of a determination of processes type according to an exemplary implementation of the disclosed teachings. 
           [0023]      FIG. 8 —is a simulation of SIEM alerts validation according to an exemplary implementation of the disclosed teachings. 
       
    
    
     DETAILED DESCRIPTION 
       [0024]    Below, exemplary implementations of the teachings will be described in detail with reference to accompanying drawings so as to be easily realized by a person having ordinary knowledge in the art. The exemplary implementations may be embodied in various forms without being limited to the exemplary implementations set forth herein. Descriptions of well-known parts are omitted for clarity, and like reference numerals refer to like elements throughout. 
         [0025]    It is important to note that the exemplary implementations disclosed by the present application are only examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed disclosures. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality. In the drawings, like numerals refer to like parts through several views. 
         [0026]    A system is connected to a plurality of user devices coupled to an enterprise&#39;s network. The system continuously collects and stores forensic data related to the enterprise&#39;s network. The system analyses the collected data. Based on the analysis, the system is able to differentiate between different processes initiated in the enterprise&#39;s network, i.e., major system processes, minor system processes and non-system processes. The differentiation between the different processes enables the system to monitor new processes and determine whether they are associated to any of the previously identified processes. Such determination enables the system to provide analytics respective of each process initiated in the enterprise&#39;s network. 
         [0027]      FIG. 1  depicts an exemplary and non-limiting block diagram used to describe the operation of the system  100  according to an exemplary implementation. A plurality of user devices (UD)  110 - 1  through  110 -N (collectively referred hereinafter as user devices  110  or individually as a user device  110 , merely for simplicity purposes), where N is an integer equal to or greater than  1 , are communicatively connected to an enterprise&#39;s network  120 . The user devices  110  can be, but are not limited to smart phones, mobile phones, laptops, tablet computers, wearable computing devices, personal computers (PCs), a combination thereof and the like. The network  120  may comprise the likes of busses, local area network (LAN), wide area network (WAN), metro area network (MAN), the worldwide web (WWW), the Internet, as well as a variety of other communication networks, whether wired or wireless, and in any combination, that enable the transfer of data between the different elements of the system  100 . 
         [0028]    A server  130  is further connected to the network  120 . The server  130  is configured to identify processes initiated in the network  120  and continuously monitoring their operation. The server  130  is further configured to analyze the processes and determine the type of each process. A type of process maybe a major system process, a minor system process and a non-system processes. System processes are any type of processes initiated at the boot of a user device  110 . System processes that are fatal for the operation of the user device  110  are considered major system processes. System processes that initiated at the boot of the user device  110  and are not fatal for the operation of the user device  110  are considered minor system processes. This differentiation between the processes enables generation of analytics respective of each process, optimization of the performance of the system  100  and furthermore, identification of security incident as further described herein below. 
         [0029]    Types of processes spawned by previous processes are too being determined by the server  130  based on their source, i.e., a process spawned by a major process, which is not a system process is considered a new, non-system process. System processes spawned by a major or a minor system process considered part of the spawning process, i.e., a major system process or a minor system process respectively. All processes spawned by non-system processes are considered continuous and therefore, part of the spawning non-system process. A process is considered by the server  130  terminated only when the user device  110  resets or all initiated and spawned processes terminated. In case a newly spawned process has no source, it shall be determined by the server  130  as a security incident. According to an exemplary implementation, the server  130  is further configured to receive alerts from security information and event management (STEM) system associated with suspicious events. The alerts can then be matched by the server  130  to processes associated therewith for determination of whether there is a security incident and to verify false positives. 
         [0030]    According to an exemplary implementation, the server  130  analyzes one or more processes initiated through the network  120  by the one or more user device  110 . The server  130  then determines, respective of the analysis, a type of each of the one or more processes, i.e., whether it is a major system process, a minor system process or a non-system process. The server  130  then continuously monitors the processes and generate analytics respective thereof. The analytics may then be stored in a database  140  for further processing. 
         [0031]      FIG. 2  is an exemplary and non-limiting schematic diagram  200  of the server  130  according to an exemplary implementation. The server  130  includes an interface  1301  to the network  120  by which the server  130  sends and receives data over the network  120 . The server  130  further includes a mining unit (MU)  1302  by which the server  130  identifies a plurality of processes initiated through the network  120  by one or more user devices  130  as further described hereinabove. According to this exemplary implementation, the MU  1302  includes the plurality of agents  115  installed on the user devices  110 . The processes are then analyzed by a processing unit (PU)  1303  to determine a type of each process. Optionally, the server  130  further includes a data storage unit (DSU)  1305  for storing the types of each process and analytics respective thereof. 
         [0032]      FIG. 3  is an exemplary and non-limiting flowchart  300  describing a method for determining types of processes and generating analytics respective thereof according to an exemplary implementation. In S 310 , the operation starts when at least one process initiated in the network  120  by a user device, for example, the user device  110 , is identified by the server  130 . 
         [0033]    According to an exemplary implementation, the MU  1302  continuously monitors and collects data and/or metadata respective of all events occurred in an operating system of a UD  110 - 1 . The PU  1303  then analyzes the events. Respective of the analysis, the PU  1303  is configured to generate a causality chain for the at least one event. The causality chain comprises all the threads that attribute to the at least one event in a chronological order. The PU  1303  then uses the causality chain to identify a main thread that led to the event, i.e., the thread that started the process that ended at the at least one event. The PU  1303  then configures the MDU  1305  to determine whether a malicious process is associated with the main thread. In case a determination was made that a malicious process is associated with the main thread, the process is marked by the PU  1303  as infected. Data respective of the process may then be sent for storage in the DSU  1304  for further processing. 
         [0034]      FIG. 3  is an exemplary and non-limiting flowchart  300  describing a method for determining types of processes and generating analytics respective thereof according to an exemplary implementation. In S 310 , the operation starts when at least one event occurred in the network  120  by a user device, for example, the UD  110 - 1 , is identified by the server  130 . 
         [0035]    In S 320 , the process is analyzed as further described hereinabove. In S 330 , respective of the analysis, a type of the process is determined. S 330  is further described herein below with respect of  FIG. 4 . 
         [0036]    In S 340 , The process is continuously monitored by the server  130 . In S 350 , one or more analytics are generated respective of the monitoring. In S 360 , the generated analytics are sent for storage in, for example, the database  140 . In S 370 , it is checked whether the process terminated and if so, execution terminates, otherwise, execution continues with S 340 . 
         [0037]      FIG. 4  is an exemplary and non-limiting flowchart  330  describing a method for determining a type of a process according to an exemplary implementation. In S 330 - 10 , it is checked whether the process was initiated in the boot of the user device  110  and if so, execution continues with S 330 - 20 ; otherwise, execution continues with S 330 - 15 . 
         [0038]    In S 330 - 15 , the process determined as a non-system type and execution terminates. In S 330 - 20  it is checked whether the process is fatal for the user device  110  operation and if so, execution continues with S 330 - 30 ; otherwise, execution continues with S 330 - 25 . 
         [0039]    In S 330 - 25 , the type of the process is determined as a minor system process and execution terminates. In S 330 - 30 , the process is determined as a major system process and execution terminates. 
         [0040]      FIG. 5  is an exemplary and non-limiting flowchart  500  describing a method for identifying security incidents respective of processes&#39; type according to an exemplary implementation. In S 510 , the operation starts when at least one process initiation is identified on, for example a user device  110  communicatively coupled to the network  120 . In S 520 , it is checked whether the process was initiated at the boot of the system and if so, execution continues with S 560 ; otherwise, execution continues with S 530 . In S 530 , it is checked whether the process was initiated by another process and if so, execution continues with S 550 ; otherwise, execution continues with S 540 . 
         [0041]    In S 540 , an alert is provided respective of a security incident. As the process was not initiated at the boot nor was initiated by another process, it is determined as suspicious and therefore the alert is provided. In S 550 , a type of the initiating process is determined as further described hereinabove with respect of  FIG. 4 . 
         [0042]    In S 560 , the type of the initiated process is determined. In S 570 , analytics respective of the file initiation and/or type are generated. In S 570 , the analytics may further be sent for storage in, for example, the database  140 . In S 580 , it is checked whether additional processes were initiated and if so, execution continues with S 510 ; otherwise, execution terminates. 
         [0043]      FIG. 6  depicts an exemplary and non-limiting diagram  600  simulating determination of a process&#39; type according to an exemplary implementation. As shown in the diagram  600 , at the boot  610 , two processes are initiated  620  and  630 . Therefore, both of the process  620  and  630  are considered system processes,  620  is a major system process as it is fatal for the operation of the system and  630  is a minor system process as its operation is not fatal for the operation of the system. Process  640  is not a system process, although initiated by the system process  620  and therefore it is considered a new flow. Process  650  which was initiated by the non-system process  640 , is determined as same flow of the process  640 . Process  660  which was initiated following the boot do not have an initiating process and therefore considered suspicious. 
         [0044]      FIG. 7  depicts an exemplary and non-limiting diagram  700  simulating identification and marking of processes throughout their execution in an enterprise&#39;s network according to an exemplary implementation. As shown in the diagram  700 , at the boot of a host, five processes are initiated  710 - 10  through  710 - 5 . The server  130  monitors the processes and determines both DLLs and threads associated with the respective process. 
         [0045]      FIG. 8  depicts an exemplary and non-limiting diagram  800  of validating alerts received from SIEM system according to an exemplary implementation. A plurality of alerts  810  are received by the server  130  from at least one SIEM system. Then plurality of alerts are then matched to associated process that spawned in Google chrome®  820 - 1  and Internet Explorer®  820 - 2 . The processes are then analyzed by the server  130  respective of the processes&#39; type and a determination of malware  830  is made respective thereof. 
         [0046]    The principles of the disclosure are implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. 
         [0047]    All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the disclosure and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and exemplary implementations of the disclosed teachings, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. 
         [0048]    A person skilled-in-the-art will readily note that other exemplary implementations of the disclosed teachings may be achieved without departing from the scope of the disclosed teachings. All such exemplary implementations are included herein. The scope of the disclosed teachings should be limited solely by the claims thereto.