Patent Application: US-78025204-A

Abstract:
method and apparatus to monitor and detect anomalies of information content flows , the method comprising the steps of capturing information access packets , filtering packets to extract information , decoding packets to determine information content , deriving content signatures , trending prototypical behavior , and detecting anomalies of information access , and said apparatus comprising a computing device comprising a network based device that captures the information and produces anomaly information .

Description:
we describe the invention of a new method and apparatus to monitor and detect anomalies of information content flows . the invention can be applied to monitor flow of information content across any network or within any application . the invention is unique in two respects — 1 . technology : monitoring and analysis is based on trending and anomaly detection at the information or content - level . there have been earlier applications of anomaly detection , but for lower - level activities such as intrusion detection ( network - layer or system - layer ), or for specific application activity monitoring such as transaction monitoring ( credit cards ). information content layer activities are much broader and complex than network - layer or system - layer activities . 2 . application : the current invention has several unique risk assessment applications in the information content security arena . a . unauthorized information disclosure : the invention can detect anomalies based on correlation of information flow , users , and time . these anomalies can be used to discover “ unauthorized information disclosures ” from confidential information repositories , without requiring to know the specific type of information being disclosed . b . content usage analysis : the invention can analyze content usage and classify content based on rare information exchanges versus common and widely shared information exchanges . this can lead to discovery of “ critical ” information assets within the organization . next , we describe the details of the invention . we believe that these details are adequate for a practitioner , skilled in the art , to develop an information assessment apparatus . classically , intrusion detection has been approached by classifying mis - use ( via attack signatures )[ escamilla , lippman et al ] or via anomaly detection . [ lapadula ] provides a good summary of various intrusion detection techniques in the literature . various techniques used for anomaly detection include using strings [ forrest et al . ], logic - based [ ko et al . ], or rule - based [ anderson et al .]. a classical statistical anomaly detection system proposed to address network and system - level intrusion detection is presented in ides / nides [ javitz , jou ]. in general , statistical techniques overcome the problems with the declarative problems logic or rule - based anomaly detection techniques . traditional use of anomaly detection of accesses is based on comparing sequence of accesses to historical “ learnt ” sequences . significant deviations in similarity from normal learnt sequences can be classified as anomalies . typical similarity measures are based on threshold - based comparators ( such as the ones used in [ lane97 , lane ]), non - parametric clustering classification techniques such as parzen windows [ fukunaga90 ], or hidden markov models [ rabiner90 ]. our problem of content - based anomaly detection has a unique challenge in that the content set itself can changes with time , thus reducing the effectiveness of such similarity - based learning approaches . instead we propose the use of higher - level behavioral models ( e . g ., memory ) to classify between anomalies and legitimate access to information . the basic architecture of the invention is indicated in fig1 . for brevity , we will refer to the invention as cmad ( content monitoring and anomaly detection ). the cmad as described is a software - based appliance installed on a network . 1 . pdu filtering — the pdu filtering module 10 inspects each packet on the network in a promiscuous mode . cmaad is assumed to be installed as a tap on the network . the packets are filtered based on a variety of layer 2 through layer 7 protocols . only meaningful packets representing “ information content ” are retained . packets representing information content are indicated by protocols and applications of interest , such as document application ( e . g ., notes , documentum , word , etc . ), data - base access protocol ( e . g ., sql — both queries and retrievals ), application protocols ( e . g ., smtp , telnet , ftp , rcp , http , etc . ), and certain file systems protocols . packets that do not meet with these criteria are discarded . 2 . content and message decoding — the content and message decoding module 12 in fig1 decodes the packets based on knowledge and semantics of the specific application or protocol , and the type of encoding used by the application . for instance , if this document were to be accessed across the network by a word application , the module would be able to decode the “ text ” words within this document as it was loaded across the network where the invention was installed . alternatively , if this document were to be emailed to an email client within the enterprise , the module would be able to decode the “ text ” words within world document , as part of an attachment to an smtp message . further , the module notes the delineation of new message boundaries , so that decoded content text words can be classified into their respective messages . 3 . content analysis and signature computation — the content analysis and signature computation module 14 in fig1 first achieves real - time mapping of message words into a content distribution vector ( called cdv ). the cdv is a quantitative representation of the content message that seeks to retain the information theoretic value of the content . one candidate method for deriving cdv is based on creating a frequency - based distribution of the key text words in the message . the module further derives a compact statistical signature from the content distribution vector , called a content signature . the content signature summarizes the content using numeric values . the key advantages of deriving a content signature is that profiling and anomaly detection can be done on the basis of statistical analysis of content signatures . 4 . cstu association and storage — the cstu association and storage module 16 in fig1 stores the content , along with the user identity from the message payload , time , and location ( source and destination ip addresses of the pdu ) into a database called the cstu database . ( cstu stands for the associated combination of content , space , time , and user ). the complete content object as stored internally is made up of three types of sub - fields — content handle , content distribution vector , content signature and additional content attributes . the high - level schema of the cstu database is shown in fig2 . examples of content handle 26 in fig2 can include the specific file name of the content , or the request query string that will result in the actual content as a response in a database transaction . the content distribution vector 28 in fig2 and the content signatures 30 in fig2 have already been described above . the content object can also include additional content attributes 32 in fig2 , which can be used for anomaly processing and additional reporting purposes . these attributes can include the content type ( e . g ., excel document vs . word document ), content length ( bytes for example ), content hash ( unique representation of the content ), content encoding information , content properties ( including ownership if relevant , time of creation , read / write / execute permissions , and encryption , password protection status ). 5 . cstu mining — the cstu mining module 18 in fig1 periodically examines cstu database and derives the “ prototypical ” model of content , users , and time . the specific technique used for cstu mining is based on statistical clustering , filtering , and distance - based metrics . alternative machine learning techniques can also be used for cstu mining , such as neural networks or rule - based expert systems . the cstu table information is periodically deleted ( aged ) from the database as configured by the administrator . the aging period is also called “ averaging interval ” and typically is on the order of several days depending on the nature of the mining algorithm , the organization , type of information being monitored , users , etc . 6 . anomaly detection — the anomaly detection module 20 in fig1 detects “ strong ” deviations from the prototypical model established by the cstu mining module . strong deviations are characterized by anomaly detection rules on various combinations of user , content , location , time entities . any anomalies are diverted to anomaly processing module for further filtering and processing . 7 . anomaly processing — the objective of the anomaly processing module 20 in fig1 is to filter the anomalies so as to minimize “ false alarms ” and increase the “ precision ” of anomalies . the anomaly processing achieves this using a variety of techniques including : a . positive correlation with past security violation events b . negative correlation with past false alarms or non - events the output of the anomaly processing modules is a report listing the anomalies , their corresponding content signatures , content handles , user ids , access time and location . this report should be comprehensive enough for security administrators to investigate the root cause behind the content anomalies . consistent anomalies that are detected close to 100 % with low false alarms can be eventually classified by “ pattern ” of misuse . such anomalies can be detected in real - time , leading to a variety of responses , including real - time alerts , request of additional validation , or denial of access . our content analysis method first involves mapping the content into a content distribution vector ( cdv ). the cdv represents the frequency of each word in the content . each word in the cdv occupies a location corresponding to its lexicographic location within the vocabulary of the enterprise . fig3 illustrates a sample cdv of content . the next step is to represent the cdv and the content with a compact content signature . a content signature should have the following properties : 1 . uniqueness — content signatures should be able to uniquely represent a certain content 2 . clustering property — content signatures should be able to “ aggregate ” similar content 3 . ordering property — content signatures should allow simple “ distance ” or “ ordering ” operations 4 . computational property — content signatures should afford easy real - time computation our approach of anomaly detection for unauthorized disclosures does not itself depend on the choice of the content signatures , so we will simply outline a set of candidate content signatures . depending on the application , the choice of one versus the other may be more appropriate . one candidate is based on moment statistics : content signatures could be simply the n - dimensional moment statistic of the cdv . thus , a 2 - dimensional content signature would consist of the mean of the cdv , and the standard deviation of the cdv . another candidate is simply the use of “ hash ” to convert content into a number . ( hash may offer semi - uniqueness , but does not offer ordering or clustering required in the list above ). alternative candidates are the use of document clustering techniques ( such as described in [ steinbech et al . ], including k - means based clustering and agglomerative hierarchical clustering ) where all the documents that classify into one cluster share the same ( or very similar ) content signatures . in general , the idea behind content signatures is to permit clustering of documents based on their content . the cstu mining framework is based on establishing a relationship between various entities including content , user , location , and time . in this invention , we use a statistical approach to develop relationship between these entities . we assume that these entities are stored in a relational form in the cstu database . the cstu mining algorithm examines the cstu database by analyzing the relationships and creating a statistical profile of the entities in three derived tables . fig4 illustrates the ucsfd , and should be self - explanatory . the ucsfd can help construct a frequency view of all the content signatures accessed by a user . fig5 illustrates the ucstd , and indicates how it can help construct a time distribution of all the content accesses by a user . fig6 illustrates the ucsld , and shows how it can help construct the location distribution all the content accesses by a user . the cstu anomaly detection framework expresses anomalies in terms of the behavioral relationships of entities such as content , users , time , and location . to devise these relationships , we will define four deviation conditions that are helpful to detect anomalies . the four deviation conditions are as following : usually , authorized access of confidential information revolves around a small set of content relevant to a user &# 39 ; s role within an organization . as organizational roles change , projects change , leading to change in activities and subsequently a change in their corresponding content signatures . however , even in cases with these changes , it is expected that a legitimate ( authorized ) information access by users will have some correlation with time . this correlation is also referred to as memory . the memory deviation condition seeks to capture information access that does not exhibit “ expected ” level of memory . such deviants are also referred to as content transients . a memory deviation condition is captured by determining for every user , and for each piece of content , the time evolution of the variable representing the frequency of content signature across each averaging interval . this evolution is referred to as the content signature frequency process , csf ( t ), in fig7 . a transient in this variable represents a memory deviation condition . fig7 shows a transient . algorithmically , a transient can be captured by determining the second derivative ( or equivalent discrete computation ) of the variable representing the frequency of content signature . if the second derivative is an outlier 1 , that is exceeds a certain memory deviation threshold mdt , a transient is declared . 1 our approach of identifying outliers is based on distance - based thresholding . threshold can be accomplished in any number of commonly known techniques — an example is setting threshold at mean ± k . σ , where mean is the sample mean of the measurements , σ is the sample standard deviation , and k is an integer threshold parameter designed around the distribution of the measurement . outlier conditions can be identified on a level - basis ( i . e . crossing the threshold ), or on a smoothing majority window - basis ( i . e . crossing level x out of y times in a sequence ), or other alternative formulations . rule : if for content cs i , then a memory deviation is said to occur at time t2 . 2 . rare content condition : usually , the authorized access of confidential information revolves around frequent access of a small set of content relevant to a user &# 39 ; s role within an organization . thus , any information content that is rarely accessed ( especially combined with other deviation conditions ) can be a good candidate to lead to a potential unauthorized disclosure activity . a rare content condition is captured by examining the user content signature frequency distribution table for each user . a rare occurrence within this table is a rare content condition . fig4 shows a rare content condition as marked by the alphabet r . algorithmically , a rare content condition can be captured by if the frequency of any content signatures falls below expected threshold of access frequency aft over the averaging interval . rule : if for user i , and content j , then the user i &# 39 ; s access of content j qualifies as a rare content condition . 3 . time deviation condition : we expect usual authorized access of confidential information to be around fairly predictable times of access , specific to a user , and users &# 39 ; role within the organization any strong deviation from the historical time of access can be a good candidate to lead to a potential unauthorized disclosure activity . a time deviation condition is detected by examining the user content time access distribution for each user . any outliers on this distribution point to time deviations . standard statistical metrics can be used to quantify outliers . fig5 illustrates an example of a time deviation condition . rule : if for user i , ucstd ij is an outlier , the user i &# 39 ; s access of content j qualifies as a time deviation condition . 4 . location deviation condition : we expect usual authorized access of confidential information to be around fairly predictable 2 locations of access , specific to a user , and users &# 39 ; role within the organization . location can be quantified by the combination of source and destination protocol addresses ( such as ip addresses ) contained within the content messages . any strong deviation from the historical addresses of access can be a good candidate to lead to a potential unauthorized disclosure activity . 2 the assumption is that even with dynamic ip address protocols such as dhcp , the typical ip addresses of desktops remain fairly static . if this is not the case , additional mechanisms such as cookies can be used to detect persistence of a specific user machine . a location deviation condition is detected by examining the user content location access distribution for each user . any outliers on this distribution point to location deviations . standard statistical metrics can be used to quantify outliers . fig6 illustrates an example of a location deviation condition . rule : if for user i , ucsld ij is an outlier , the user i &# 39 ; s access of content j qualifies as a location deviation condition . the foregoing merely illustrates the principles of the present invention . those skilled in the art will be able to device various modifications , which although not explicitly described or shown herein , embody the principles of the invention and are thus within its spirit and scope . the above mentioned invention has been implemented in a specific embodiment . one instance of definition of criticality information 72 on the iam is by means of a graphical user interface , as shown in fig4 . the iaa is implemented on user computers and generates results that are uploaded to the iam . fig5 shows one embodiment of the results when uploaded to the iam and viewed by the graphical user interface on the iam . fig5 ( a ) 74 shows the color coded organization level critical information , 5 ( b ) 76 shows the distribution of critical information , 5 ( c ) 78 shows the distribution of critical information at a computer level , and 5 ( d ) 80 shows the details of critical information collected from a specific iaa . 1 . escamilla t ., intrusion detection : network security beyond the firewall , john wiley & amp ; 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