Patent Application: US-63659909-A

Abstract:
the invention provides methods and systems for detecting exploits . a received file is examined to determine whether or not it corresponds to any of one or more predetermined models of normal file types . if the received file does not correspond to any of the one or more predetermined models of normal file types , it is flagged as a potential exploit .

Description:
embodiments of the present invention are generally concerned with a method that allows for the detection of novel viruses in non - executable files : in particular ( although not necessarily exclusively ), hand - crafted file content designed to trigger vulnerabilities in a software application ( e . g . web - browser , pdf viewer , etc .) or operating system component which will lead — directly or indirectly — to the execution of machine - code instructions . in general , in the embodiments described in more detail below , we model the contents of a type of file ( a “ file - type ”) as a sequence with one or more latent markov properties , of arbitrary order , which represent the structure of the file , and which can be combined with probability distributions conditioned on the state of those markov variables to model the file &# 39 ; s contents . this is known as a factorial hidden markov model [ ghrahramani 1997 ], though in the case of a single markov chain , it reduces to a standard hidden markov model . we infer a given file - type &# 39 ; s structure within this probabilistic framework , and then use this model to infer the normality of the content of a file of that type . this measure of a file &# 39 ; s normality is used by a decision engine to determine if the file is likely to be an exploit . in some embodiments , again as described in more detail below , a heuristic is used to accelerate the inference of a file - type &# 39 ; s structure within the framework of a hidden markov model and a factorial hidden markov model . in a little more detail , we have developed novel methods and systems which can detect exploits , including exploits in non - executable files such as those described in the background section above . in stark contrast to the prior art methods , the approach taken by embodiments of the present it ) invention does not rely for its effectiveness on known examples of infected files : rather it uses probabilistic techniques to develop a model of what a file should normally contain ( i . e . a model of a ‘ normal ’ file ), and uses this probability model to detect outliers , i . e . files that are not normal . this approach proposed by the present inventors is effective because exploits in non - executable files tend to rely on abnormal file content to e . g . trigger an invalid state in a viewer ; additionally , the hand - crafted construction of the file and the presence of machine - code segments lead it to stray from the norm . hence a file which is not probable ( i . e . does not fit with the probability model of a normal file of that type ) is likely to be a virus . in preferred embodiments of the invention , a file is modelled as a series of one or more segments . each segment is modelled as a sequence of integer values . for each possible file - type , the structure governing each segment &# 39 ; s contents is inferred by a machine learning system . in some embodiments the structure may be hierarchical , so that a particular value at a particular point in a segment depends on two or more structural variables . in other embodiments the structure may be flat , so that a particular value at a particular point in the segment depends only on a single structural variable . segmentation schemes , where used , may be specified manually by the developer . typically , most files will not in fact be segmented , in which case the entire file &# 39 ; s structure is inferred . where the file is segmented ( e . g . jpeg images with optional metadata blocks ) segmentation is performed manually , and the structure of the individual segments is inferred . in the embodiments described in more detail below , the structure of a file is inferred in the context of a particular probabilistic model termed a factorial hidden markov model , which consists of one or more “ hidden ” markov chains , encapsulating the structural variables and the probabilities of moving from one part of the structure to the next , and several probability distributions conditioned on the current state ( s ) of the structural variable ( s ) which govern the particular integer value observed at a point in the file or segment &# 39 ; s structure . in the case of a flat structure , where only one markov chain is used , the model reverts to a hidden markov model . the system uses a novel heuristic to construct a prototype model to be trained , which experts in the field term an informative prior , to expedite the training process . an optimisation strategy based on longest common sub - sequences to optimise training performance and to help capture long - range correlations in data is used . this provides an alternative to other methods in the field , such as the use of entropic priors ( see e . g . u . s . pat . no . 6 , 212 , 510 ). in execution , the system uses a rapid - lookup mechanism , identifying byte - sequence values associated with a plurality of file - types in conjunction with several heuristics to rapidly select a shortlist of plausible file - types for a particular file , which in some embodiments functions in the absence of file - name or file mime - type information . for each type in this short - list , the probability of the file being of that type is then inferred using the method described in the preceding paragraphs . the system uses these per - file - type threshold - probabilities in conjunction with a decision function to infer , give the probability of a file &# 39 ; s content output from a model , whether the file is normal , is possibly infected , or is definitely infected . such decisions can then be used in conjunction with an overall intrusion - detection , virus - filtering or virus scanning apparatus to control access to the file ( e . g . the information may be used to determine if a file should be deleted , quarantined , passed to the client with a warning , or passed to the client without a warning ). the use of probabilistic models to detect outliers is typically called “ anomaly detection ”, and such techniques have been used in the past in academic research into intrusion detection systems . such methods are applied to network traffic , rather than the files transmitted by network traffic , and take advantage of the developer &# 39 ; s pre - existing knowledge about the structure of such traffic to identify features that can then be classified using , for example , clustering or rule - learning techniques [ munson 2006 , chan 2003 ] however as with script and executable inspection , these methods require pre - existing knowledge of the structure of network transmission , and in particular rely heavily on the fixed and simple nature of network transmission structure , as opposed to the more flexible , complex and repeating structure commonly found in file formats . in practice , most applied work in intrusion detection is still based on the idea of ‘ honeypots ’ ( u . s . pat . no . 5 , 440 , 723 ) and pattern ( i . e . signature ) recognition techniques applied to network traffic ( u . s . pat . no . 5 , 278 , 901 , u . s . pat . no . 6 , 279 , 113 , u . s . pat . no . 6 , 405 , 318 ). in contrast to these prior art applications for probabilistic models , the methods proposed by the present inventors do not assume pre - existing structure of the file - type . to cater for this , we use factorial hidden markov models [ ghahramani 1997 ] ( fhmms ) to automatically infer the hierarchical structure of the file - type in a probabilistic model , which can then be used to detect anomalies , in the manner described in more detail below . it should be noted that a factorial hidden markov model with a single hidden markov chain reduces to a simple hidden markov model [ rabiner 1986 ] ( hmm ), and as an optimisation , a system may choose to use the somewhat simpler inference algorithms associated with hidden markov models in such cases . hmms , and latterly fhmms have been applied to the fields of speech recognition ( e . g . u . s . pat . no . 7 , 209 , 883 ) and image recognition , but this is the first time that they have been applied to the problem of inferring file structure , particularly with a view towards anomaly detection and novel virus detection . the detailed discussion below of embodiments of the invention is broken down into four parts . in the first part we discuss segmentation . in the second part we discuss the probability model in detail , and the algorithms for performing inference on that model . in the third part we discuss the method of model creation and training , and the heuristics employed by that method . in the final part we discuss the file classification process , in which models are selected and executed to infer the probability of the file being of a particular type , and the decision function used to output a discrete outcome based on those probabilities . in most cases a file &# 39 ; s format is fixed . however some types of files contain various optional segments in optional order : the resulting file can be thought of as a meta - file containing multiple embedded files which in aggregation represent the totality of the data . examples of this include : mp3 files which may contain some or all of id3v1 and id3v2 metadata segments , identifying the artists , song etc ., in addition to the binary music stream ; jpeg files which may contain icc colour profiles , exif metadata identifying camera and date details , xmp metadata listing transformations and others in addition to binary image data ; png files which similarly may contain icc and metadata (“ itxt ”) segments ; and movie container files such as wmv which may contain metadata and binary data in one of several different formats . extracting these embedded “ micro - files ” is called segmentation , and the micro - files themselves are called segments . thus while for most files ( e . g . animated cursor files , wmf files , etc .) the method infers the structure of the file as a whole , for files with optional embedded segments ( e . g . jpeg , png , mp3 and wmv files ), the system represents the file as a collection of segments , and the method infers the structure , of each of these segments . the segmentation is explicitly specified by the developer . in the following , unless otherwise stated , we will use the term “ file ” to refer to an actual file , or a segment within a file , as described hitherto . files do not necessarily have to reside on disk , they may be reconstituted in memory from a network communication . for the purposes of performing inference , a sample of bytes from the start of the file is converted to a sequence of integers . the size of the sample is determined in training , as described in the next section . in one embodiment , the sequence of integers has a one - to - one correspondence with the raw bytes of the file , being a sequence of values between 0 and 255 of the same length as the number of bytes in the sample . in another embodiment , a sequence of integers is generated by reading two bytes at a time to form a value between 0 and 65536 , and moving one byte forward at each iteration ( leading to an overlap of bytes read ). in general the system provides for user - defined variation between the number of bytes read for each integer in the sequence ( the word - size ) and the number of bytes by which the system moves through the file before reading in the next integer value ( the step - size ). in addition to these methods , a third embodiment involves mapping of variable - length byte - sequences to individual integer values , a process known as tokenization , using a manually specified dictionary mapping variable - length byte sequences to their integer values . our model posits that there is a latent , higher - order , structure governing the generation of values observed in this sequence . moreover , we posit that there is a temporal dependence in our model : viz . that the state of the structural variable ( s ) changes as we progress through the sequence . a standard model used to model temporal sequences with latent variables is the hidden markov model ( hmm ), which models sequence values as being generated by a number of so - called emission probability distributions conditioned on a latent multinomial variable with a markov property . recent work has extended the traditional markov model so that there can be more than one latent markov chain : such models have been termed factorial hidden markov models ( fhmms ). it is possible to estimate the posterior distribution ( i . e . “ train ”) these models using expectation propagation , sampling techniques such as gibb &# 39 ; s sampling , or by generating an equivalent hidden markov model with a single markov chain and using the expectation maximisation algorithm . fig4 illustrates an fhmm with two latent markov chains . to describe the mathematical components of a factorial hidden markov model , we shall first start with the simpler case of the standard hidden markov model . in this model , the joint probability of an observation , y t and state s t at time t ( or at a position , t , in a sequence ) is where p ( s 0 ) is the probability of the system starting from state s ; and p ( s t | s t - 1 ) is the probability of transitioning to state s t having previously been in state s t - 1 , and p ( y t | s t ) is the “ emission ” probability of seeing the observed value y t when the hidden chain is in state s t . the state is a single multinomial variable which can take one of k discrete values , implying that a k × k transition matrix is sufficient to model the transition probability distribution p ( s t | s t - 1 ). if the observables are discrete symbols taking on one of l values , the emission distribution p ( y t | s t ) can be modelled by a k × l matrix . if the emission distribution is continuous , or if it is very large and requires approximation , other distributions can be used , such as the guassian distribution , a mixture of guassian distributions , or other functional approximation schemes such as , but not limited to , linear regression and neural networks . alternately , one could seek to create a simple transition matrix , but store a compressed version , for example . by quantizing the probabilities . for the purposes of illustration , in the case of a conditional mixture of guassians , the distribution p ( yt | s t ) would be modelled as where m is the total number of component distributions in the mixture , μ ( m ) and σ ( m ) are the mean and standard deviation of the distribution respectively , and weighting parameter w ( m ) is the probability p ( m − m | s ) that the observable was generated by mixture component m conditioned on the current state s . the factorial hidden markov model extends this initial model by conditioning the observations on more than one hidden states , effectively converting s from a scalar to a vector parameter . each dimension of the state vector s ( d ) may take k d values , in order to ensure the model is tractable , we assume the dimensions of the state vector are independent of one - another , i . e . where d is the dimensionality of the state vector ( i . e . the number of latent markov properties , or “ chains ”). consequently this new state distribution can be plugged into the existing hmm framework , without any further changes to the underlying mathematical structure , though clearly inference becomes significantly more challenging . 1 . identifying recurring byte - strings in the files &# 39 ; segments to aid in training 2 . identifying recurring byte - strings in initial bytes of the files overall to aid in file - type detection 3 . constructing a prototype model and training it based on the patterns extracted in step ( 1 ) for steps one , and two we employ the longest common sub - string ( lcs ) algorithm ( gusfield , dan [ 1997 ] ( 1999 ). algorithms on strings , trees and sequences : computer science and computational biology . usa : cambridge university press . isbn 0 - 521 - 58519 - 8 ), which is not to be confused with the longest common subsequence algorithm . as part of the algorithm , we compute the longest common suffix for all pairs of prefixes of strings . the longest common suffix for strings s and t of length p and q respectively is defined as for the sample strings “ abab ” and “ baba ” this results in a table such as that shown below . note that we leave boundaries before and after the words , with all entries set to zero by default . from analysis of this table , we can identify sub - strings . these will occur a number on a table is less than the number in the table cell above and to the left of it . the number in that upper left cell , is the length of the sub - string found , and its index can be found by subtracting the length from the column index of the cell in question . the addition of a boundary at the end of the input strings allows us to determine sub - strings at the end of the input strings . note that whereas the original algorithm sought to find the longest sub - string , we seek to find all sub - strings whose length is greater than a certain value ( e . g . 4 characters ). further , as we only need the previous rows content to do this , we do not have to store the entire table in memory , only the current and previous rows . we use this method to detect the longest common sub - strings in the file . the input strings are not the raw file - contents , rather they are the sets of integer values generated from the file contents . for reasons of efficient these strings are stored in a particular tree - structure known as a trie ( a retrieval tree ). this is a tree structure with one node per character , which allows for the efficient storage of several strings , using an implicit prefix code . at the leaves of the trie , denoting the ends of individual strings , we store the average index in the sequence where the strings have been found , and the number of times the strings have been found . for further efficiency , a compressed or “ patricia ” trie is typically used . having found all sub - strings common to all pairs of files in the corpus , we then seek to extract from them a minimal set of - sub - strings , such that no sub - string in the set is itself a sub - string of any other sub - string . 1 . a new trie is created for results . 2 . the first available string , hereafter called the probe , and its associated data , is removed from the input trie . 3 . a search is performed of the input to find the first string which shares at least one sub - string with the probe string , using the longest - common sub - strings method described earlier 4 . if no such pattern is found , the probe is added to the list of results 5 . if such a pattern is found , the following steps take place i . all common sub - strings are removed from the probe , and added to the input . their average offset and frequency in the file are computed from the probe and matching string &# 39 ; s information . ii . all remaining sub - strings from the probe are added to the input , with appropriate offset and frequency information . iii . the matching string is removed from the input list . iv . all common sub - strings are removed from the matching string v . all remaining sub - strings from the matching string are added to the input , with appropriate offset and frequency information . 6 . if the input trie is empty , return the result trie , else jump to ( 2 ) and repeat empirically it has been observed that trimming leading and trailing zero values from such strings is beneficial , as zero - bytes are often used as padding . additionally , it may be necessary to re - scan the input files to correct rounding - errors in the offset and frequency calculations . once frequency information has been calculated , it can be used to select a certain short - list of strings used to initialise the model . the strings read in for each segment are also used to determine the number of bytes to sample from that segment during classification and training , being set to the sum of the position of the last sub - string found , the length of the last sub - string found , and the average length of a gap between sub - strings , with appropriate conversion from integer sequence size and raw byte - size within the underlying file . for the purposes of file - type detection , this process is performed on the unsegmented overall file , on raw bytes instead of integers generated from bytes , and the resulting list of byte - sequences is stored alongside the description of the file - type . byte - sequences which start at offset zero — sometimes called magic numbers — are stored in one of n arrays , where n is the size of the greatest byte - sequence length acceptable for a magic - number code . this information is used for file - type detection , as described further below . one significant novel contribution to the field made by embodiments of the present invention is the use of character patterns extracted by repeated execution of an lcs algorithm to derive a prototype hidden markov model ( factorial or simple )— which may be considered an informative prior by those skilled in the art — which is used in training to accelerate convergence of the learning algorithm . this prototype allows for even simple hidden markov models to capture long - range correlations in sequences ; the failure to do so is an issue that often affects hidden markov models . additionally it acts as a useful optimisation in the training process , dramatically accelerating convergence . the input is the set of common integer sub - strings found in the data , which have offset and frequency data associated with them . typically the n most frequent such integer sub - strings , or patterns are used , where n is a tunable parameter of the algorithm . the next steps depend on whether the model has one or two hidden markov chains . we begin by considering the case of a model with one latent markov chain with a single markov chain , each hidden state corresponds to one or more positions in the input file . we start with an empty model , and for each pattern , add states to capture that pattern , and states to capture the content that typically follows those patterns . for patterns , and in the trivial case , we create a single state in the model for every value in the pattern . for each of these states the emission distribution is configured so it is sharply peaked in the area of that particular value in the pattern . in one embodiment a discrete distribution is used , to with a high value probability p e set for the byte value and a uniformly low probability assigned to all other values . “ high ” in this context depends on the particular embodiment , in one case we have set it to 75 %. it is important that it not be too high , to allow for flexibility in the posterior - density estimation process . the transition probabilities for each state set the probability of moving to the next state to a high probability , p t . the remaining probability mass is divided equally amongst all other possible transitions . the value of p t depends on the particular embodiment , typically it is set to a value around 75 %. thus the most probable path through particular states representing a pattern is a straightforward progression from first to last , emitting the values in the byte - sequence . we then add a number of states ( called gap - states ) after these pattern states to capture the probability distribution of those values seen after the pattern . the number depends on the embodiment : it may be fixed , or it may be prorated according to the number of values between the average offset of the current pattern in the file , and the average offset of the next pattern . the emission distributions between these gap states distribute the probability mass uniformly amongst all possible values . for each gap state , the probability of it transitioning back to itself is set to the probability of it moving forward to its immediate neighbour is set to and the probability of transitioning to any other state is set to where n is the total number of states in the model . the transition probability for the last of the gap states is configured to leap to the first of one of m subsequent byte - string state sequences , where m depends on the embodiment , and is typically set to 3 ( except where less than m subsequent sequence - states exists ). so for the last state in a sequence of gap - states , the probability of transitioning back to itself is the probability of it transitioning to each of the next three patterns is set to and the remaining probability mass is divided equally amongst all other possible transitions . it is generally preferable that the number of states added to represent patterns be held to some limit . in cases where the length of the pattern is greater than the number of states , the following simple algorithm is designed to set the ideal number of states to represent a pattern : this should , if possible return the maximal number of states such that the final state will be configured to match the final value in a pattern , e . g . if the maximum number of pattern states is 5 and the minimum is 3 , and a pattern is 6 values long , the ideal length is 3 , which ensures that the last state encodes the last value ( as well as the third value of course ). the transition probabilities for the last pattern state are altered to include the loop back to the start . if the last state encodes the last value in the pattern , the probabilities of moving to the first gap state , and of looping back to the first pattern state , are both set to if the last state does not encode the last value in the pattern , its probability of moving back to the first pattern state is set to p t with all other state probabilities divided uniformly . the pattern which does encode the last value of the pattern has a probability of moving to the next pattern state , and of moving to the first gap state , set to the remainder of the probability mass is divided equally among all other possible transitions . the emission probabilities for each pattern are altered to take into account the various values in the pattern those states should match . typically this involves setting the probabilities for those values to where n is the number of times one passes through that state while matching the pattern . as currently discussed there has been no mention of the case where patterns overlap . in actuality this case is dealt with by the fact that prior transition probability , and the probability of transitioning from the end of each gap state - sequence to the next pattern state - sequence , is split to over m states as discussed earlier . in the case where there are two markov chains , such as found in a simple factorial hidden markov model the approach taken is similar . the first markov chain can be thought of as representing the fields in the file and has one state per pattern . for each state , the probability of transitioning from one state to the next is set to a reasonably high value , p t ( 1 ) with the probabilities of transitioning to all other states set to where n 1 is the number of states in this first markov chain . a second markov chain is used to represent positions in the field . its size is fixed beforehand , being an implementation detail of the embodiment , being equivalent to the maximum number of pattern and gap states to be used for any pattern . transition probabilities are altered to provide a bias toward forward movement , but not to the same extent as in the single - markov case , so that in this case p t . 2 & lt ;& lt ; p t . 1 . the same process used to configure the emission distribution p ( y | s ) in a model with a single markov chain is then used to configure the probability distribution p ( y | s 1 , s 2 ) with two markov chains , however instead of assigning probability values , the values are initialised to zero and then the augmented by p e for each emission , for each state , for each pattern . the resulting distributions are then normalised over the total number of patterns . this prototype model can then be used with an appropriate posterior density estimation algorithm such as expectation propagation , sampling techniques such as gibb &# 39 ; s sampling or in the case of a model with a single hidden markov chain , expectation maximisation . 1 . identifying a short - list of probable file - types ( fig1 ); 2 . evaluating the probability of a file being of a particular type for each type in the short - list ( fig2 ); and step one involves detecting the file - type from a file &# 39 ; s contents . optionally the file - name extension , the mime - type or both may also be used to determine ( or aid in the determination of ) file type . fig1 illustrates this step . in the basic case , which we will describe initially , we assume that mime - type and file - name information is unavailable . in the following we will use the term magic - number to refer to a sequence of one or more bytes , always found at the start of a file of a particular type , by which the type of the file can be identified ( a class of information sometimes termed in - band metadata ). we will use the term pattern to refer to any sequence of one or more bytes typically , though not always , found within a file , which can aid in the identification of the file &# 39 ; s type . it follows therefore that a magic - number is just a special case of a pattern . the system contains a database of n sorted tables of magic numbers . the first table consists of one - byte magic numbers , the second table consists of two - byte magic numbers and so on until the n th table consists of n - byte magic numbers . each magic number entry in each table is matched to one or more file - type records , containing information about file - type ( s ) identifiable by that magic number . on receipt of a file , a certain number of the files bytes are read into a stream . the following algorithm is then performed on the stream contents : as can be seen , this simply performs a binary search through all possible magic number lists , finding the types that match , and adding them to a list with a given probability . in a typical embodiment , the list is sorted by probability , with most probable types being the first returned by a standard enumeration of the lists contents . the probability of a given type matching a stream is calculated by performing a search for the patterns registered with a given file - type , using the boyer moore algorithm . in cases where it is known patterns always occur at a fixed offset , a simpler algorithm may be used . patterns may match in whole , in part , or not at all . an interim probability of matching is calculated by simply dividing the number of correctly matched bytes , across all patterns , divided by the total number of bytes in all patterns . in cases where patterns overlap , if one pattern matches , the other is not counted in the total . if neither match , the longer pattern is used in calculating the total length of all patterns . in order to take the number of bytes to match into account ( after all , matching one out of two bytes is less impressive than matching 10 out of 25 ), a null - probability is used to weight matches as follows : where n match is the number of bytes that matched in patterns , n total is the total number of bytes that can match , as described earlier , and n value is the total number of possible values . in order to prevent a different problem , of partial matching to over specified file - types exceeding full matching to typically - specified file - types , we calculate the mean and standard deviation of n total across all possible file - types , set n max total = m total + s total and alter the definition of the null probability so that it is defined as this heuristic approach allows us to rapidly select and sort by likelihood of match a shortlist of possible file - types . once we have exhausted all possible magic numbers , we consider those few file - types for which no magic numbers are defined , and use the same process to calculate their probability and add them . where file - name and / or mime - type information is available , this can be used to immediately discard file - types which fail to match the extension or mime - type , thus avoiding costly probability calculations . to avoid false - positives it is necessary to take into account typical mistakes in file - names or mime - types that occur , particularly on the internet . this also helps avoid costly probability calculations . while discarding types without due consideration carries risks , it is offset by the fact that virus writers will always supply the correct mime - type in network communications , or file - name extension in file - based viruses , so that the appropriate program whose vulnerabilities they seek to exploit are used to attempt to display the file . typically only a subset ( e . g . the first five ) of the matching file - types are used in the next stage , classification . having selected a short - list of matching types , each type is considered in turn . for each type , a list of segments is extracted ; for simple unsegmented types , a pseudo - segment representing the entire file is returned . for each segment of each file - type , the appropriate classifier is selected from the database of training classifiers . a classifier is a segmentation strategy . the segmentation strategy will determine how to segment the file , and contains evaluation logic for each segment . the evaluation logic for each segment is a trained model ( as described earlier ), a method of converting the segment &# 39 ; s bytes to integers to be evaluated by that mode , and a threshold probability with which the probability output by the model can be compared . the contents of the segment in the incoming file are converted to a sequence of integer values , as described hitherto , and then classified by the classifier using the forward algorithm . the viterbi algorithm may also be used in some embodiments : it provides a more accurate estimation of the probability , albeit at the expense of longer classification times . an issue arises however , in that hidden markov model methods implicitly assume sequences of fixed length . sequences of shorter than expected length will result in a greater probability than sequences of expected length , which is the opposite result to that expected . to work around this , the natural log of the probability output by the model ( often referred to as the log - likelihood ) is divided by the square of the sequence length . the resulting probability is stored in a record of classification results , which is then used by the final , decision stage . the decision process effectively states that a file is abnormal if and only if for all matching file - types are least one segment is abnormal . this allows a fast - succeed decision process to be executed in the classification stage , which will cease further inspection and come to the decision that a file is normal if all segments of a particular type are deemed normal . the task therefore is to determine what constitutes an abnormal file . the approach taken by embodiments of this invention is as follows . having used a corpus of normal files in the training phase to estimate the posterior parameters of the model , the model is then used to classify all files , and the probability score for each of these normal files are recorded . the mean score is calculated , as is the standard deviation amongst all scores . a threshold is set at the mean minus a certain number of standard deviations , which in a typical embodiment is set to 2 . 7 standard deviations . in subsequent use in the detection of exploits , should the probability of a file &# 39 ; s contents ( given the assumption that it belongs to a particular segment of file of a particular type ) be less than this threshold , it is deemed abnormal , by the aforementioned assumptions of segment and type . the segment or segments of a given file are compared in this manner with each of the short - listed file types . if all of the segments of the file are found to be within the respective thresholds for the segments of one of the short - listed file types , then the file is deemed a normal file ( of that type ) and the results for the other short - listed file types can be ignored . if , on the other hand , one or more of the file segments is outside the thresholds for each of the short - listed file types , then the file is deemed abnormal and appropriate action can be taken ( e . g . deletion of quarantine of the file ). a pleasant consequence of this approach , is that having calculated the threshold , the files in the corpus ( i . e . those use for training ) can then be compared to the determined threshold . should any of the files fall below this threshold , it may indicate that : 1 . the file is not of the particular type ostensibly represented by the corpus ( e . g . accidentally including a gif file in a corpus of jpeg files ); 3 . the model has failed to generalise due either to a limited number of sample files , limited variation within sample files , or both . thus the training process itself is robust in the face of shoddy or erroneous methods for the establishment of a corpus of files of a particular type , and furthermore can communicate to the developer the stability of the model . such feedback can inform the choice of a single or double markov chain in the “ hidden ” part of the model . a second threshold can be calculated , greater than the abnormal threshold ( e . g . mean minus two times standard deviation ). files which fall between this and the main threshold are not blocked , but their presence is noted so that further inspection may be carried out in future , and explicit viral signatures developed to match them should they turn out to be exploits . more elaborate threshold schemes , expressed in terms of multiples of standard deviation , may be developed at the discretion of the implementer to implement more complex or granular policies . so in short , as will be apparent from the discussion above , embodiments of the invention typically comprise : 2 . representing a file as a sequence of integers , according to an appropriate encoding scheme ; 3 . constructing a hidden markov model with one or two hidden markov chains using the techniques ; 4 . estimating the posterior distribution of the model using a sequence of training files , none of which are infected , using standard methods known to those skilled in the art , as for example described in [ ghahramani 1997 ], or in the case of a single markov - 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