Patent Publication Number: US-11048822-B2

Title: System, apparatus and method for anonymizing data prior to threat detection analysis

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. application Ser. No. 14/994,954, filed on Jan. 13, 2016, now U.S. Pat. No. 10,657,286, which claims the benefit of U.S. Provisional Application No. 62/103,448, filed on Jan. 14, 2015, and U.S. Provisional Application No. 62/197,485 filed on Jul. 27, 2015, the entire contents of which are incorporated by reference. 
    
    
     1. FIELD 
     Embodiments of the disclosure relate to the field of cyber security. 
     2. GENERAL BACKGROUND 
     Over the last few years, the general populous has encountered the proliferation of malicious software (sometimes referred to as “malware”) over the Internet. Malware has many forms including exploits, namely information that attempts to take advantage of a vulnerability in software that is loaded onto an electronic device in order to adversely influence or attack operations of that electronic device. Despite repeated efforts through advanced detection systems and software patches to address software vulnerabilities, malware continues to evade and infect electronic devices worldwide. 
     In combatting the spread of malware, it has become paramount that a vast amount of information associated with network traffic, which is propagating to/Born/within an enterprise network over a prolonged period of time, is analyzed for malware. This stored information offers immeasurable value for incident response testing so that security personnel can better understand when and how a network breach (e.g., malware infection of one or more endpoint devices within an enterprise network) occurred within an enterprise (e.g., a company, governmental agency, or other entity) in order to address current security issues associated with the enterprise network. 
     Normally, incident response testing is handled by persons outside of the enterprise such as a contracted, cyber security service provider. In some cases, incident response testing may pose a security risk as well, especially when the stored information supplied for testing includes personally identifiable information (PII). “PII” is information that can be used to identify a specific user to which the information pertains. Examples of different types of PII include, but are not limited or restricted to user names, phone numbers, home addresses, machine names, and/or social media account names. 
     For instance, without being anonymized, the PII is now accessible to persons outside of the enterprise which, by itself, creates a security risk that such information may be used inappropriately. Another security risk is that there is no defined access hierarchy for PII that is part of the stored information. Rather, anyone with access to the stored information also has access to the PII. Lastly, as the stored information including the PII is subsequently stored for incident response testing, there is a risk that systems associated with the incident response testing may become compromised, thereby gaining unauthorized access to the PII. 
     Anonymization may be accomplished by obfuscating the sensitive information, such as PII for example. Such obfuscation may involve conducting cryptographic operations (e.g., encryption decryption, one-way hashes, etc.) on the sensitive information. However, in response to various events, such as cryptographic keying material becoming compromised or customer preferences, such keying material would need to be changed. Currently, this change would require cryptographically secured information, perhaps millions or even billions of entries within stored network traffic, to be re-encrypted. Depending on the frequency in changing of the keying material, this could be a challenging task for cyber security service providers. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the invention are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which: 
         FIG. 1  is an exemplary block diagram of a communication system deploying a multi-tier security framework. 
         FIG. 2  is an exemplary block diagram of the security framework of  FIG. 1 . 
         FIG. 3  is a general block diagram of the interoperability of a management system and the security framework of  FIG. 2 . 
         FIG. 4  is a first exemplary embodiment of the operational flow for maintaining anonymized data and effectively handling re-obfuscation of the anonymized data within the security framework of  FIGS. 1-2 . 
         FIG. 5  is an exemplary embodiment of a logical representative of the network sensor engine of  FIGS. 2-4 . 
         FIG. 6A  is an exemplary embodiment of a first logical storage element represented as a Personally Identifiable Information (PII) reference table. 
         FIG. 6B  is an exemplary embodiment of a second logical storage element represented as a Eflow table. 
         FIG. 6C  is an exemplary embodiment of the first logical storage element represented as the PII reference table after undergoing a global obfuscation change. 
         FIG. 6D  is an exemplary embodiment of a third logical storage element generated based on a query from a root user for information associated with detected threats. 
         FIG. 7  is a second exemplary embodiment of the operational flow for maintaining anonymized data and effectively handling re-obfuscation of the anonymized data within the security framework of  FIGS. 1-2 . 
         FIG. 8A  is an exemplary embodiment of a first logical storage element represented as a Personally Identifiable Information (PII) reference table. 
         FIG. 8B  is an exemplary embodiment of a second logical storage element represented as the PII reference table with an identifier generated based, at least in part, on the PII data. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments of the disclosure relate to a system, apparatus and method for (i) receiving a flow of information that includes anonymized data (hereinafter referred to as an “eflow”) and, where appropriate, (ii) generating an identifier that represents the anonymized data. The identifier is generated for anonymized data from which subsequent recovery of the anonymized data as cleartext is desired. 
     Instead of being stored as the collection of anonymized data and non-anonymized data received by the analysis engine from one or more network sensor engines, an eflow is stored with one or more identifiers in lieu of corresponding anonymized data. Stated differently, anonymized data within an eflow is extracted and substituted with its corresponding identifier. The correspondence between the anonymized data and its identifier is maintained separately from the stored flows. As a result, in response to a change in the obfuscation scheme, such as a change in cryptographic keys that are used in the generation of the anonymized data from cleartext for example, the anonymized data would need to be subsequently modified. However, the stored flows would not need to be modified as the identifier(s) would remain unchanged. 
     More specifically, a flow may include a single segment of anonymized data that is associated with a particular classification or multiple segments of anonymized data which may be associated with different classifications. Examples of classifications for anonymized data may include, but is not limited or restricted to the following classes: (1) user data (e.g., user name, device name, group name, etc.), (2) contact data (e.g., home address, phone number, former residence information, etc.), and/or (3) passcode data (e.g., password, personal identification number “PIN”, etc.). 
     An identifier is uniquely assigned to a segment of anonymized data when recovery of the non-anonymized data may be needed later in time. In some cases, identifiers may not be assigned to certain anonymized data classifications because recovery of that the non-anonymized data (cleartext) is not desired. For instance, a password that is anonymized may not be assigned an identifier when recovery of the password in cleartext is not desired for system configuration. Rather, this particular anonymized data is maintained as part of the eflow. 
     According to one embodiment of the disclosure, upon receipt of an eflow, an analysis engine analyzes each segment of anonymized data to determine if cleartext recovery from that anonymized data is desired. If so, for each segment, the anonymized data is extracted from the eflow and an identifier is generated for that anonymized data. Thereafter, each unique segment of extracted anonymized data along with its corresponding identifier is stored in one or more entries of a first logical storage element (e.g., a first table, a first storage region, etc.). The remaining non-anonymized data within that particular eflow (and anonymized data for which cleartext recovery is not desired) and the identifier(s) are stored in one or more entries of a second logical storage element (e.g., a second table, a second storage region, etc.). After a prolonged period of time, the number of populated entries of the second logical storage element, which include at least the non-anonymized data for a particular node (e.g., user, device, etc.), will greatly exceed the number of populated entries within the first logical storage element for that particular node, normally at least a factor of 10 5 . 
     Hence, a change in the obfuscation scheme involves an alteration of the anonymized data within the first logical storage element. However, no alteration of data within the second logical storage element is required, which reduces the amount of system complexity needed in handling changes in the obfuscation scheme such as changing cryptographic keys. 
     Of course, according to another embodiment of the disclosure, the anonymized data within a particular eflow along with a corresponding identifier may be stored in a first region of memory while the non-anonymized data within that particular eflow and the identifier are stored in a second region of memory. Again, after a prolonged period of time, the number of entries, which correspond to the number of stored eflows, will greatly exceed the number of entries needed for the unique anonymized data uploads. 
     Herein, the “anonymized data” includes customer sensitive information, such as personally identifiable information (PII) for example, that is obfuscated. PII may be obfuscated by conducting cryptographic operations on the sensitive information using keying material. Where it is desirable for the data to be available for recovery in cleartext, the cryptographic operations may involve encrypting some or all of the sensitive information using keying material that allows for cleartext recovery (e.g., one or more cryptographic keys). 
     Additionally, where cleartext recovery is unnecessary (e.g. passwords), the cryptographic operations may include (1) conducting a one-way hash operation on the sensitive information (where the sensitive information cannot be recovered and no identifier is assigned for the hashed information) or (2) assigning an arbitrary generic value (e.g., a predetermined number of alphanumeric characters or symbols “***”) or even a random or pseudo-random value to obfuscate the sensitive information. 
     The “identifier” includes a representation (e.g., a string of alphanumeric characters and/or symbols) for a node to which the flow of information pertains, but obfuscates the actual identity of the node. For instance, as an illustrative embodiment, where the sensitive information constitutes a user name (Bob Smith), the anonymized data may result in an anonymized value (125FE35) while the identifier may be auto-generated as a particular user number User256). Any subsequent change in the keying material may cause the anonymized data to change (125FE35→679F032), but the identifier (User256), which is used as a reference (e.g., a source, destination, etc.) for one or more eflows that may undergo subsequent malware analysis, perhaps by logic within the analysis engine  220 , remains unchanged. 
     According to an illustrative embodiment of the disclosure, a network sensor engine receives input information from at least one source and anonymizes sensitive information, such as personally identifiable information (hereinafter “PII”) associated with the received input information. Resultant information, referred to herein as an “eflow”, represents a flow of the received input information that pertains to a particular node. According to this embodiment, the eflow comprises (1) an eflow identifier, (2) anonymized data (e.g., anonymized PII) and (3) non-anonymized data (e.g., one or more attributes of stored and/or captured information that may be anomalous). The eflow is uploaded from the network sensor engine to an analysis engine, which determines whether the anonymized PII within the eflow already corresponds to a particular identifier. 
     According to one embodiment, the anonymized PII and its corresponding identifier already may be maintained within a first logical storage element (e.g., a PII reference table stored in a memory). If the anonymized data already corresponds to a particular identifier, the particular identifier is used to reference the eflow identifier and the attribute(s) associated with that eflow instead of the anonymized PII (e.g., identifier substituted for the anonymized PII). If the anonymized PII does not correspond to any identifier, the analysis engine generates an identifier for the eflow and subsequently relates that newly generated identifier to the anonymized PII. The newly generated identifier is used to reference the eflow identifier and attribute(s) associated with the eflow. 
     Thereafter, any changes in the obfuscation scheme, such as changing in keying material that is used to generate the anonymized PII for example, does not require information within the second logical storage element (e.g., eflow table stored in memory) to be altered. Rather, only the anonymized PII within the PII reference table needs to be re-anonymized using the new keying material. 
     I. Terminology 
     In the following description, certain terminology is used to describe features of the invention. For example, in certain situations, the terms “sensor”, “logic,” and “engine” are representative of hardware, firmware and/or software that is configured to perform one or more functions. For instance, as hardware, a sensor (or logic or engine) may include circuitry having data processing and/or data capturing functionality combined with data transmission and/or storage functionality. For instance, a sensor (or logic or engine) may include data processing circuitry such as a processor (e.g., digital signal processor, microprocessor with one or more processor cores, a programmable gate array, a microcontroller, an application specific integrated circuit, etc.), circuitry with storage functionality such as semiconductor memory, and/or circuitry with data transmission functionality such as wireless or wired transmitter and/or transceiver circuitry. 
     Alternatively, the sensor (or logic or engine) may be software in the form of one or more software images or software modules, such as executable code in the form of an executable application, an application programming interface (API), a routine or subroutine, a script, a procedure, an applet, a servlet, source code, object code, a shared library/dynamic load library, or one or more instructions. The software module(s) may be stored in any type of a suitable non-transitory storage medium, or transitory storage medium (e.g., electrical, optical, acoustical or other form of propagated signals such as carrier waves, infrared signals, or digital signals). Examples of non-transitory storage medium may include, but are not limited or restricted to a programmable circuit; a semiconductor memory; non-persistent storage such as volatile memory (e.g., any type of random access memory “RAM”); persistent storage such as non-volatile memory (e.g., read-only memory “ROM”, power-backed RAM, flash memory, phase-change memory, etc.), a solid-state drive, hard disk drive, an optical disc drive, or a portable memory device. As firmware, the executable code is stored in persistent storage. 
     The term “data” is broadly defined as information, normally in digitized form. Hence, data may include control information or management information. 
     The term “flow” broadly refers to a grouping of information. For instance, a flow may include series of related packets in transit or in storage. A “packet” generally refers to information transmitted in any prescribed format, such as a standard packet, a frame, an asynchronous transfer mode (ATM) cell, or the like. One type of flow is referred to as “eflow”, which is a grouping of information of which a segment (i.e. portion) of information includes anonymized data. 
     The series of related packets forming an eflow may feature one of more attributes representing one or more basic properties and/or characteristic of the flow. The attributes may be associated with an executable element or a non-executable element such as a dynamically link library (DLL), a Portable Document Format (PDF) file, a JavaScript® file, Zip® file, a Flash® file, a document (for example, a Microsoft® Office® document, Word® document, etc.), an electronic mail (email), downloaded web page, a text message, or the like. 
     “Keying material” generally represents information, which may be used as a key or used to generate a key that is utilized by a cryptographic function to obfuscate input information. 
     A “node” normally represents a particular person or a particular device that is traceable to the particular person or a particular group of persons. 
     The term “transmission medium” is a physical or logical communication path between two or more network devices (e.g., one of the network devices being an endpoint device with data processing and network connectivity) such as, for example, a server; a mainframe; a firewall; intermediary devices such as a router, a switch or a bridge; or a client device such as a desktop or laptop computer, netbook, tablet, smart phone, set top box, wearable computing device, or a video game console). For instance, the communication path may include wired and/or wireless segments, and/or shared memory locations. Examples of wired and/or wireless segments include electrical wiring, optical fiber, cable, bus trace, or a wireless channel using infrared, radio frequency (RF), or any other wired/wireless signaling mechanism. 
     The term “computerized” generally represents that any corresponding operations are conducted by hardware in combination with software and/or firmware. Also, the term “match” generally describes that a certain level of comparison has been successfully achieved. 
     Lastly, the terms “or” and “and/or” as used herein are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and/or C” mean “any of the following: A; B; C; A and B; A and C; B and C; A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive. 
     As this invention is susceptible to embodiments of many different forms, it is intended that the present disclosure be considered as an example of the principles of the invention and not intended to limit the invention to the specific embodiments shown and described. 
     II. Exemplary System Architecture 
     Referring to  FIG. 1 , an exemplary block diagram of a communication system  100  deploying a multi-tier security framework (system)  110  is shown. The security framework  110  is adapted to monitor and analyze information associated with network traffic  120  that is routed over transmission medium  130  that partially forms an enterprise network  140 . According to one embodiment of the disclosure, the security framework  110  receives, processes and/or stores input information associated with communications occurring within the enterprise network  140 . 
     As shown, the security framework  110  may be communicatively coupled with the transmission medium  130  via a network interface  150 . In general, the network interface  150  operates as a data capturing device (sometimes referred to as a “tap” or “network tap”) that is configured to receive information propagating to/from one or more endpoint devices  170   1 - 170   M  (M≥1) and provide at least some of this information to the security framework  110 . For instance, the network interface  150  may provide a series of packets or certain information within the packets. Alternatively, although not shown, the security framework  110  may be positioned in-line with the endpoint device(s)  170   1 - 170   M  without the network interface  150 . As another alternative, the network interface  150  may be part of the security framework  110 . 
     Herein, as an illustrative example, the input information may include information associated with a plurality of packets forming incoming network traffic received via a communication network  160 . The communication network  160  may include a public network (e.g., Internet) in which case one or more security appliances, such as a firewall for example, are positioned to receive and process network traffic prior to receipt by logic within the security framework  110 . Alternatively, the communication network  160  may be a private network such as a wireless data telecommunication network, wide area network (WAN), a type of local area network (LAN), or a combination of networks. As other illustrative examples, the input information may include log information, one or more flow based collections such as netflow (e.g., OSI Layer 4 “L4” information regarding communications monitored by other network devices), and host telemetry information (e.g., information pertaining to one or more endpoint devices  170   1 - 170   M ), as described below. 
     Although  FIG. 1  illustrates the multi-tier security framework  110  within the enterprise network  140 , it is contemplated that at least certain logic forming the multi-tier security framework  110  may be operate outside the enterprise network  140 . For example, at least some of the functions of the multi-tier security framework  110 , such as operations associated with a (data) analysis engine  220  of  FIG. 2  for example, may be performed as cloud computing services (e.g., analysis engine  220  situated in the “cloud” and accessible via the Internet or other WAN). This is illustrated by a portion of the multi-tier security framework  110  being positioned outside the enterprise network  140 . 
     According to one embodiment of the disclosure, as illustrated in  FIG. 2 , the security framework  110  comprises one or more network sensor engines  200   1 - 200   R  (R≥1) and an analysis engine  220 . Deployed at various locations within the enterprise network  140  (e.g., campus, data center, etc.), each of the one or more network sensor engines (also referred to as “network sensor engine(s)”)  200   1 - 200   R  may be configured to receive, process and/or store input information. The input information may comprise one or more of the following: (i) log information  272  from different network devices (e.g., third-party log sources  270  such as Active Directory® server logs, Domain Name System “DNS” server logs, Dynamic Host Configuration Protocol “DHCP” server logs, etc.); (ii) information associated with monitored incoming packets  275  that form network traffic, including extracted objects (e.g., files); (iii) flow records  282  from third-party flow record sources  280 ; and (iv) host telemetry information  284  from one or more endpoint sensors  210   1 - 210   S  associated with one or more endpoint devices  170   1 - 170   M  (e.g., client devices and/or servers). 
     As further shown in  FIG. 2 , the network sensor engine(s)  200   1 - 200   R  include at least a first network sensor engine  200   1  that is adapted to communicate with one or more endpoint sensors  210   1 - 210   S  (S≥1), which collect and/or store information associated with the endpoint devices  170   1 - 170   M . These endpoint sensor(s)  210   1 - 210   S  may be configured as lightweight software sensors deployed on the endpoint devices  170   1 - 170   M , where the endpoint sensor(s)  210   1 - 210   S  are adapted to gather information associated with one or more of the endpoint devices  170   1 - 170   M  and provide host telemetry information  284  to a particular network sensor engine (e.g., first network sensor engine  200   1 ) of the network sensor engine(s)  200   1 - 200   R . 
     For example, the host telemetry information  284  for a particular endpoint device (e.g., endpoint device  170   1 ) may include registry settings and/or registry changes of that endpoint device, running processes list for that endpoint device, memory usage information for that endpoint device, network connection information for that endpoint device, operating system patch level for that endpoint device, files modified since the last update, and/or disk/file system activity of that endpoint device. The gathered host telemetry information  284  may be especially useful when the endpoint device  170   1 - 170   M  is infected. For example, network connectivity information may be gathered that is associated with a malicious attack in the form of an unintended communication with a malicious Command and Control (CNC) server. Also, information associated with disk/file system activity may be gathered that is associated with a malicious attack to obtain sensitive data stored on a persistent storage device (e.g., computer hard drive, server, etc.). 
     There may be different endpoint sensors deployed for different devices (e.g., an endpoint sensor for a server endpoint device may be configured to gather different information than an endpoint sensor for a client endpoint device). By way of a specific example, where the endpoint device  170   1  is a server, the host telemetry information  284  may include application logs that indicate a history of applications running on the server, active network connections of the server, files modified on the server, hash of critical files of the server, information (e.g., file name, access date/time, etc.) that has been exfiltrated, or the like. 
     Furthermore, the first network sensor engine  200   1  is adapted to receive network packets propagating to/from one or more devices in the network (e.g., information to/from endpoint devices  170   1 - 170   M  or other network devices). In some embodiments the first network sensor engine  200   1  is adapted to extract or generate network sensor data from the network packets. For example, in a specific embodiment, the first network sensor engine  200   1  is adapted to perform deep packet inspection (DPI) on the packet captures  275  to extract metadata from L2-L7 headers. For example, the first network sensor engine  200   1  may extract headers associated with Hypertext Transfer Protocol (HTTP) messages. 
     Furthermore, the first network sensor engine  200   1  may be adapted to receive log information  272  from one or more remotely located servers (e.g., Active Directory® server, DNS server, DHCP server, etc.) that may form part of the enterprise network  140  or operate in concert with network devices within the enterprise network  140 . Herein, the “log information”  272  includes information pertaining to events that have been recorded during communications between the remotely located servers and various endpoint devices. In some embodiments the first network sensor engine  200   1  is adapted to extract and/or generate metadata from the log information  272 . 
     As an example, the first network sensor engine  200   1  may be adapted to receive log information  272  from any of the third-party log sources  270  such as an Active Directory® server, which enables the first network sensor engine  200   1  to generate a user/Internet Protocol (IP) address mapping. Since IP addresses are dynamic and may be re-assigned and the security framework is capable of storing data for a prolonged time period, the user/IP address mapping enables the first network sensor engine  200   1  to determine a particular user (and her corresponding endpoint device) that was previously assigned a particular IP address at a certain period of time and that endpoint device may have been compromised by malware. 
     As other illustrative examples, the first network sensor engine  200   1  may be adapted to receive log information  272  from a DNS server, which provides the first network sensor engine  200   1  with DNS requests made. Also, the first network sensor engine  200   1  may be adapted to receive log information  272  from the DHCP server, which may be used to generate a device/IP address mapping. Combined with the user/IP address mapping, the user and device assigned to a particular IP address over the prolonged period of time may be uncovered for that IP address despite reassignment of the IP address during the prolonged period of time. 
     Besides log information  272 , the first network sensor engine  200   1  may be adapted to communicate and receive flow records (e.g., netflow records, show records, flow records, etc.)  282  from third-party flow record sources  280 , namely information associated with communications received and/or monitored by other networks devices within the enterprise network  140  (e.g., IP address(es), port number(s), transport type, statistics concerning the network connection, etc.). The flow records  282  enable the analysis engine  220  (or network sensor engine  200   1  itself) to formulate a threat exposure mapping (e.g., display of communication paths undertaken by network devices within the enterprise network  140 ), which may be used to detect anomalous communication patterns through deviations in normal communications by one or more of the network devices, such as an endpoint device (e.g., client device or server) for example. In some embodiments the first network sensor engine  200   1  is adapted to extract and/or generate metadata from the flow records  282 . 
     During receipt and processing of input information as described above, the first network sensor engine  200   1  may be adapted to obfuscate (i.e., anonymize) at least some of the input information by anonymizing sensitive or personalized information associated with a user to which the input, information pertains. For instance, the first, portion of the input information may include personally identifiable information (PII) associated with the user (e.g., user name identifying the user, device name identifying the endpoint device, group name identifying a group of users, etc.). Of course, it is contemplated that the first portion of the input information may include another PII classification (e.g., contact data) other than user data. The anonymized PII along with the non-anonymized data associated with the eflow is provided to the analysis engine  220 . 
     Normally positioned to reside outside the enterprise network  140  of the particular customer, as shown in  FIG. 2 , the analysis engine  220  is communicatively coupled to the network sensor engines  200   1 - 200   R  and receives eflows from each of the network sensor engines  200   1 - 200   R , where each eflow includes the anonymized PII and non-anonymized data (e.g., attributes, raw data associated with the input information in a form of logs, flow records, packet captures, or host telemetry). As an illustrative example, for HTTP traffic monitored by the network sensor engines  200   1 - 200   R , the network sensor data may include attributes within HTTP messages, where some of these attributes may undergo anonymization (e.g., Host names, Uniform Resource Indicator “URI” or Uniform Resource Locator “URI”, etc.) while other data may remain non-anonymized data (e.g., Mime-type, Filename, etc.). 
     Although not shown in  FIG. 2 , the analysis engine  220  may be deployed outside of the enterprise network  140  of a particular customer, such as in a private cloud or in a public cloud. However, it is contemplated that the analysis engine  220  may be deployed within the enterprise network  140 . As stated above, the analysis engine  220  may be implemented as hardware, software and/or firmware. For instance, the analysis engine  220  may correspond to functionality that is part of processing circuitry or software executed by processing circuitry located with the same appliance as network sensor engine  200   1 - 200   R  or in a different network device remotely located therefrom. 
     Herein, the, analysis engine is adapted to (i) provide open Application Programming Interface (API) access to the stored network sensor data and (ii) conduct analytics on data within the received eflows. The analytics may be directed to conventional analytics, ad hoc analytics and predictive analytics as set forth in U.S. patent application Ser. No. 14/743,892 entitled “System, Apparatus and Method for Prioritizing the Storage of Content. Based on a Threat Index”, the entire contents of which are incorporated by reference. 
     The analysis engine  220  may be configured to collect and anonymize data (as needed for PII) as well as transmit the data to a centralized controller (not shown) for facilitating global threat intelligence. Hence, analysis engines of different customers may be communicatively coupled to the centralized controller. The information transmitted from the analysis engines to the centralized controller may be less and/or different than the information transmitted from the network sensor engines to their corresponding analysis engines. 
     Web proxy  420  provides high-level (root) administrator  430  access to PII within a first logical storage element (e.g. PII reference table described below) as cleartext and further provides searching functionality according to one or more PIT classifications. Web proxy  420  also allows low-level (non-root) administrator  440  access to PII within the first logical storage element; however, some or all of the PII remains as anonymized data. Such operations are described in more detail with respect to  FIG. 4 . 
     Referring to  FIG. 3 , in order to provide unified management of the security framework  110 , a management system  300  may be communicatively coupled and provide control information  310  to the endpoint sensor(s)  210   1 - 210   S , network sensor engines  200   1 - 200   R  and/or analysis engine  220 . Herein, according to one embodiment of the disclosure, the management system  300  is responsible for provisioning, monitoring operability and overall management of the sensor(s)  210   1 - 210   S , the network sensor engines  200   1 - 200   R , and/or the data analysis engine  220 . For instance, the provisioning may include conducting and managing software upgrades in order to increase the speed and ease of deployment and configuration of the security framework  110 . Likewise, monitoring operability may include performing, in a periodic or aperiodic manner, health checks of the endpoint sensor(s)  210   1 - 210   S , network sensor engines  200   1 - 200   R  and/or analysis engine  220 ; collecting log information and performance data; and providing dashboards about overall health of the security framework  110 . 
     III. Exemplary Operation Flow 
     Referring now to  FIG. 4 , a first exemplary embodiment of the operational flow for maintaining anonymized data and effectively handling re-obfuscation of the anonymized data is described. Herein, as shown, the security framework  110  comprises the network sensor engine(s)  200   1 - 200   R  communicatively coupled to the analysis engine  220 . Network sensor engine(s)  200   1 - 200   R  may be configured to receive, process and/or store input information  400 , which may include log information  272 , network packet captures  275  obtained in network traffic to/from an endpoint device (not shown), flow records  282 , and/or host telemetry information  284  associated with the endpoint devices. Additionally, the network sensor engine(s)  200   1 - 200   R  are configured to produce one or more eflows  410  based on the input information  400 . Certain segments within the eflow  410  may be designated for sensitive information. 
     For instance, as an illustrative example, upon monitoring data transmitted from a particular source, network sensor engine  200   1  may generate the eflow  410  that includes a first segment  411  with anonymized data representing a user name for the source. Collectively, or in the alternative, where the monitored data is a Voice over Internet Protocol (VoIP) call, the network sensor engine  200   1  may generate the eflow  410  that includes a second segment  412  with anonymized data representing the phone number associated with the VoIP call. Data associated with these segments  411  and  412  is obfuscated (anonymized) based on a selected obfuscation scheme for that PII classification. 
     Referring now to  FIG. 5 , an exemplary embodiment of a logical representative of the network sensor engine  200   1  of  FIGS. 2-4  is shown. Herein, the network sensor engine  200   1  comprises engine management logic  500  communicatively coupled to a packet processing engine  510  and storage logic  570 . Herein, the engine management logic  500  is responsible for monitoring and/or uploading changes in operability of the network sensor engine  200   1 , such as changes in obfuscation schemes (e.g., different cryptographic algorithms for use, different Exclusive-OR “XOR” schemes, different generic character or symbol substitution schemes, etc.) to a policy engine  520  of the packet processing engine  510 . The policy engine  520  pushes policy changes in obfuscation (anonymization) to the anonymity logic  530 . As stated above, the network sensor engine  200   1  may correspond to the functionality provided by processing circuity such as one or more processors, where the policy engine  520  and/or anonymity logic  530  correspond to hardware, software executed by processor circuitry within the processor(s), or firmware. 
     Additionally, the engine management logic  500  may be responsible for controlling the storage of input information  400  within data store  580  and the formulation of eflows from the stored data. Such storage may involve extraction of sensitive information (e.g., user names, device names, phone numbers, residential address, etc.) from the input information and storage within data store  585 . Data store  580  and  585  may be logical representations (e.g., software drivers) that control the reading/writing to the physical hardware storage situated within an electronic device including the network sensor engine  200   1  or situated remotely from the network sensor engine  200   1 . 
     As shown, the packet processing engine  510  is a multi-threaded process that is at least responsible for managing anonymity operations conducted by anonymity logic  530 , which includes PII identification logic  540 , PII classification logic  545 , and PII obfuscation logic  550 . As shown, PII identification logic  540  is responsible for identifying and extracting personal identifiable information (PII) from prescribed segments within different types of input information  400  (e.g., network packet captures, log information, flow records, and/or host telemetry information). 
     The PII classification logic  545  accesses the PII extracted by the PII identification logic  540  and determines what classification such PII belongs. For instance, where the PII is a user name, the PII may be assigned to the “user data” classification. The assignment of the classification associated with the PII is used, by the PII obfuscation logic  550 , to determine what obfuscation scheme  560  to use in anonymizing the PII and subsequent placement of the anonymized data into the eflow  410 . 
     Referring back to  FIG. 4 , after the PII has been classified and anonymized, the eflow  410  is ready for transmission to the analysis engine  220 . Upon receipt of the eflow  410 , the analysis engine  220  extracts the anonymized data  411  (e.g., “125FE3B” of  FIG. 6A ) from the eflow  410  and determines (1) what data classification the anonymized data belongs and (2) is the anonymized data previously stored. According to one embodiment, this may involve a determination as to whether the anonymized data (125FE3B)  411  is already stored in a first logical storage element  600 , such as a PII reference table for example, which is stored within internal memory of the analysis engine  220  or data store external to but accessible by the analysis engine  220 . If the anonymized data is not previously stored in the PII reference table  600 , the analysis engine  220  generates an identifier (e.g., “User256” of  FIG. 6A )  610  that now corresponds to the anonymized data (125FE3B)  411  and stores the identifier (User256)  610  as part of the eflow  410  in a second logical storage element  650  such as an eflow table of  FIG. 6B . The identifier (User256)  610  now references an eflow identifier  660  (e.g., “23567” of  FIG. 6A ) for the eflow  410  along with its attributes  670 , which include information pertaining to different basic and/or characteristic of the eflow  410  although are merely identified by different alphanumeric characters for illustrative purposes. An exemplary embodiment of the PII reference table  600  and the eflow table  650  are illustrated in  FIGS. 6A-6B . 
     As additional eflows are received by the analysis engine  220 , the same operations occur in which the PII reference table  600  is populated with unique anonymized data along with unique identifiers associated therewith. As shown in  FIG. 6A , the PII reference table  600  may include all classifications of the anonymized PIIs or a plurality of PII reference tables may be used where each PII reference table stores anonymized data associated with a particular PII classification. 
     Referring still to  FIG. 4 , the analysis engine  220  may enter into a maintenance state in which an anonymization update in conducted on the anonymized data. For instance, the customer may request a cryptographic key update based on a number of factors such as the current cryptographic key has been compromised, a certain amount of time has elapsed, or increased security through a system wide key update is desired. Herein, the update may occur in accordance with a variety of update schemes, including the following: (1) cryptographic key is uploaded to the analysis engine  220  from an enterprise or (2) the PII reference table  600  is downloaded to the enterprise for modification of the anonymized entries of the PII reference table  600  at the enterprise. Thereafter, the modified. PII reference table  600  is uploaded for subsequent use by the analysis engine  220 . 
     Regardless of how conducted, the anonymization update is conducted by use of the current keying material to recover the PII in a non-obfuscated state, namely entirely or partially as cleartext. This may include decrypting the anonymized data with the current keying material. Thereafter, the PII in the non-obfuscated state is re-encrypted with the new keying material to produce re-anonymized data, which is substituted for the anonymized data. The corresponding identifier, however, is not modified. Hence, the reference between a particular PII type and its corresponding eflow attributes remain as shown in  FIG. 6B . 
     As an illustrative example, as shown in  FIGS. 4 &amp; 6A-6C , a customer requests a cryptographic key update for a global obfuscation change from use of a first keying material (K 1 ) to use of a second keying material (K 2 ). Herein, the update may occur by an enterprise uploading the keying material (K 1 ,K 2 ) to the analysis engine  220  to automatically handle the anonymization update. 
     The analysis engine  220  using the first keying material (K 1 ) decrypts a first entry  620  of anonymized data (125FE3B) within the PIT reference table  600  to recover the user name (Bob Smith). Thereafter, the user name (Bob Smith) is re-encrypted with the second keying material (K 2 ) to produce re-anonymized data (679FD32)  630  for placement into the updated PII reference table  640 . These operations continue for the remaining anonymized data (e.g., 367AC9F; 489BD82) to produce the updated anonymized data (4975FFE; 543DA92). However, for each of these updated anonymized data (679FD32; 4975FFE; 543DA92), the analyzer reference identifiers (User256; User180; User533) remain unchanged. Hence, the reference between anonymized data and its corresponding non-anonymized data for the eflows is retained. 
     IV. Data Retrieval 
     Referring back to  FIG. 4 , an exemplary embodiment of the operational flow for accessing anonymized data is described prior to the global obfuscation change. Herein, as shown, the PII reference table  600  of  FIG. 6A  may include a user interface (UI) reference identifier  615 , where the analysis engine  220  assigns an identifier different than the anonymized data identifier  610  for display by non-root users  440  requesting access to the stored eflow data. 
     More specifically, in response to a request for eflow data from a non-root user  440  (e.g., a cyber-security service provider separate from the enterprise, low-privileged administrator within the enterprise, etc.), the web proxy  420  generates a query  450  to the analysis engine  220  for stored eflow information associated with a particular HI reference name (e.g., “User51”  617  of  FIG. 6A ). In response, the analysis engine  220  identifies the corresponding anonymized data identifier (e.g., “User256” of  FIG. 6A )  610  and accesses the stored eflow information  680  associated with that anonymized data identifier  610 . The stored eflow information  680  is subsequently routed as a return message  460  to the web proxy  420 , which provides the eflow information  680  in a displayable format to the network device controlled by the non-root user  440 . The non-root user  440  is able to search for eflow information based on a particular UI reference identifier (e.g., User51) or based on a portion of the UI reference that may be common to two or more UT reference identifiers (e.g., “User5*”—common to both “User51” &amp; “User57”). 
     Alternatively, in response to a request for eflow data from a root user  430  (e.g., high-privilege administrator within the enterprise, etc.), the web proxy  420  generates a query  470  to the analysis engine  220  for stored eflow information  680  associated with a particular UT reference name (e.g., User51  617  of  FIG. 6A ), In response, the analysis engine  220  identifies the corresponding anonymized data identifier (e.g., User256)  610  and accesses the stored eflow information  680  associated with that anonymized data identifier. The stored eflow information  680  along with corresponding entry data from the PII reference table  600  and keying material is subsequently routed in a response  480  to the web proxy  420 , which recovers the cleartext version of the anonymized user name (Bob Smith) and provides the eflow information along with the cleartext user name associated with the eflow in a displayable format to the network device controlled by the root user as shown in  FIG. 6D . 
     The web proxy  420  builds a UI Table  690  that is similar to the PII Reference table  600  of  FIG. 6A , but includes cleartext versions  692 ,  694  &amp;  696  of the anonymized data  620 ,  622  and  624  for subsequent access by a root user  430  and partial access by the non-root user  440 . The root user  420  is able to search for eflow information based on a particular user name e.g., Bob Smith as well as by UI reference identifier. As stated above, portions of the cleartext version of the anonymized data and/or the UI reference identifier that is commonly shared by multiple user names (e.g., Bob S*, captures Bob Smith, Bob Sanders, etc) and/or UI reference identifiers (e.g., User5*; common to both User51 &amp; User57) may be searched by the root user. 
     Referring to  FIG. 7 , a second exemplary embodiment of the operational flow is described for generating and maintaining anonymized data and obfuscated identifiers, which are based on personally identifiable information (PII) data that is obfuscated to at least partially form the anonymized data. Herein, as shown, the security framework  110  comprises the network sensor engine(s)  200   1 - 200   R , which are communicatively coupled to the analysis engine  220  and configured to receive, process and/or store input information  700 , which may include log information  272 , network packet captures  275 , flow records  282 , and/or host telemetry information  284 . The network sensor engine(s)  200   1 - 200   R  are further configured to produce one or more eflows  710  based on the input information  700 . Certain segments within the eflow  710  may be designated for sensitive information. 
     For instance, as an illustrative example, upon monitoring data transmitted from one or more sources, network sensor engine  200   1  may generate the eflow  710  that includes a first segment  711  with anonymized data, such as obfuscated data representing an employee&#39;s name for example. Collectively, or in the alternative, the eflow  710  may include other anonymized data, such as a second segment  712  with obfuscated data representing the social security number for that employee. Data associated with each of these segments  711  and  712  is obfuscated (anonymized) in accordance with a selected obfuscation scheme for that PII classification, where the obfuscation scheme used to anonymize data for the first segment  711  of the eflow  710  may differ from the obfuscation scheme used to anonymize data for the second segment  712 . 
     Additionally, the network sensor engine  200   1  may generate an identifier  713  (hereinafter referred to as a “back-up identifier”), which is based on certain PII data that is part of input information  700  and is uniquely associated with a particular user (e.g. employee name, phone number, social security number, employer identification number, etc.). According to one embodiment of the disclosure, the back-up identifier  713  may be a keyed hash result, namely a hash value generated upon conducting a one-way hash operation based on both the PII data and a key associated with the entity to which the PII data pertains. For example, the key is static and may be associated with a particular enterprise (e.g., company, a division within a company, etc.) that is undergoing incident response testing and supplying input information  700 . 
     Referring still to  FIG. 7 , after the PII data has been classified and anonymized, the eflow  710  is ready for transmission to the analysis engine  220 , which may be on premise or remotely located (e.g., in the cloud). Upon receipt of the eflow  710 , the analysis engine  220  extracts the anonymized data  711  (e.g., “125FE3B” of  FIGS. 8A and 8B ) from the eflow  710  and determines (1) what data classification the anonymized data belongs and (2) is the anonymized data previously stored. According to one embodiment, this may involve a determination as to whether the back-up identifier (e.g., HASH_1  810 ) is already stored in a first logical storage element  800  such as a first PII reference table for example. 
     If the back-up identifier (Hash_1  810 ) is previously stored in the first PII reference table  800 , the analysis engine  220  may overwrite the stored anonymized data within an entry  820  of the first PII reference table  800  with the extracted anonymized data (125FE3B)  711  or stored the extracted anonymized data (125FE3B)  711  into an additional entry (not shown) within the first PIT reference table  800  that corresponds to HASH_1  810 , as shown in  FIG. 8A . As a result, the back-up identifier (Hash_1  810 ) now corresponds to the anonymized data (125FE3B)  711 . Although not shown, different attributes acquired from the flow  710  may be stored with the back-up identifier  810  and the anonymized data  711 . 
     If the back-up identifier (Hash_1  810 ) is not previously stored in the first PII reference table  800 , the analysis engine  220  creates a new entry (e.g., entry  820 ) within the first PIT reference table  800 , where the back-up identifier (Hash_1  810 ) now corresponds to the anonymized data (125FE3B)  711 . 
     As further shown in  FIGS. 7 and 8B , a second PII reference table  830  may be generated to include a user interface (UI) reference identifier  840 , where the analysis engine  220  assigns an identifier (e.g., “USER51”  845 ) different than the back-up identifier  810  for display by a non-root user  430  of  FIG. 7  who is requesting access to the stored eflow data, as previously described. Hence, the first PII reference table  800  allows for recovery and detection of the particulars associated with the stored anonymized data in response to corruption of the second PII reference table  830 . 
     In the foregoing description, the invention is described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention.