Patent Document

RELATED APPLICATIONS 
     The present application is a Continuation of U.S. patent application Ser. No. 13/862,849 filed on Apr. 15, 2013; 
     which in turn is a Continuation of U.S. patent application Ser. No. 13/371,455 filed on Feb. 12, 2012, which has issued as a U.S. Pat. No. 8,423,572 on Apr. 16, 2013; 
     which in turn is a Continuation of application Ser. No. 13/270,632 filed on Oct. 11, 2011, which has issued as a U.S. Pat. No. 8,117,229 on Feb. 14, 2012; 
     which in turn is a Continuation of application Ser. No. 13/155,212 filed on Jun. 7, 2011, which has issued as a U.S. Pat. No. 8,069,183 on Nov. 29, 2011; 
     which in turn is a Continuation of U.S. patent application Ser. No. 13/011,395 filed on Jan. 21, 2011, which has issued as a U.S. Pat. No. 7,987,196 on Jul. 26, 2011; 
     which in turn is a Continuation of U.S. patent application Ser. No. 12/605,357 filed on Oct. 25, 2009, which has issued as a U.S. Pat. No. 7,899,842 on Mar. 1, 2011; 
     which in turn is a Divisional from the parent U.S. application Ser. No. 11/678,587 filed on Feb. 24, 2007 to Boyce entitled “Fast identification of Complex Strings in a Data Stream”, which has issued as a U.S. Pat. No. 7,630,982 on Dec. 8, 2009; 
     the entire contents of the above noted applications and issued patents being incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to detection of complex strings in a data stream. 
     BACKGROUND 
     Fast search techniques are needed in many computing and network applications such as search engines and network addressing. Regular search of a string in a dictionary of strings of fixed sizes is rather simple, using for example binary search. With a dictionary of variable-size strings, the matching process becomes more intricate. A string of arbitrary size in which each character is uniquely defined in an alphabet is colloquially called an “exact string”. A string of arbitrary size in which at least one character may be replaced without changing the purpose of the string is colloquially called an “inexact string”. The search for an inexact string is complicated. For example searching for a name such as “John Winston Armstrong” in a dictionary of names is much simpler than searching for any name in the dictionary that contains a string such as “J . . . ton.Arm”, where ‘.’ may represent any of a subset of characters in the alphabet. In the latter, each of a large number of strings such as “Jane Clinton-Armbruster” and “Jack Newton Armstrong” is considered a successful match. 
     Numerous software-based techniques, suitable for implementation in a general-purpose computer, for fast matching of exact strings in which each character is uniquely defined and corresponds to a pre-defined alphabet are known. The Aho-Corasick algorithm, for example, is known to be computationally efficient and may be used in real-time applications, see, e.g., a paper by Alfred V. Aho and Margaret J. Corasick “Efficient String Matching: An Aid to Bibliographic Search” published in the Communications of the ACM, June 1975, Volume 18, Number 06, p. 333-340. Software-based techniques for matching “inexact strings” are also known, but are too slow for certain real-time applications such as network security applications which require fast execution, see, e.g., a paper by Ricardo A. Baeza-Yates and Gaston H. Connet “A New Approach to Text Searching” published in Communications of the ACM, 35, October 1992, p. 74-82. 
     Regular Expressions, as described, for example, in the paper written by Ken Thompson “Regular Expression Search Algorithm” published in Communications of the ACM, Vol. 11, Number 6, June 1968, p. 419-422 are commonly used for representing inexact strings. Regular expressions can be implemented efficiently using special-purpose hardware. However methods for efficient implementation of regular expressions in a general-purpose computer are yet to be developed. Software implementations of regular expressions either require a memory of extremely large size or execute in a non-bounded time which is a function of the number of such inexact strings to be considered, the complexity of the individual inexact strings, and input data to be examined. 
     One solution adopted in prior art is to use a two-stage algorithm where an algorithm for simple search, such as the Aho-Corasick algorithm, is used to efficiently find parts of packet data, which contain some part of the patterns of interest, and then a slower regular-expression-based algorithm is applied to a potentially lesser number of patterns to detect inexact patterns. Such a solution can handle a large variety of inexact patterns but has significant drawbacks including: (a) unpredictable computation effort to determine the existence, or otherwise, of a matching inexact string, the processing time being a function both of the data content and of the size and complexity of the patterns; (b) incomplete pattern identification where only a part of a pattern may be found without readily defining the boundaries of the pattern in an examined data stream—verifying a match with regular expressions may require access to a large amount of preceding data up to the possible start point, and may require waiting for data that has not yet been received; c) a requirement for post-processing to detect patterns in order of occurrence as neither the start nor end points may be known in advance, forcing ensemble matching and sorting. 
     Network intrusion detection and prevention is concerned with protecting computer systems from unintended or undesired network communications. A fundamental problem is in determining if packets in a data stream contain data strings of specific patterns (also called signatures) which are known to exploit software vulnerabilities in the computer systems. The number of such signatures of practical concern is very large and their structure is rapidly changing. Many of these signatures cannot practically be expressed as ordinary sequences of characters. For example a credit-card number uniquely identifies a specific credit card while a string comprising common digits of the numbers of all credit cards issued by one bank does not uniquely identify a specific credit card. 
     A string inserted in a data stream may be harmful to a recipient of the data stream and, hence, the need to locate the string to enable further corrective actions. Clearly, any means for detecting strings of special interest in a continuous data stream has to be sufficiently fast. One approach for fast detection is to devise special-purpose hardware circuitry with concurrent processing. However, considering the fast pace of network changes, a solution based on special-purpose hardware may be impractical. 
     A software solution is highly desirable because of its low cost, ease of deployment, and ease of adapting to the changing communications environment. There is therefore a need for a software-based algorithm that can detect a large set of strings under execution-time constraints and memory limitations in order to enhance Intrusion prevention systems (IPS) and intrusion detection systems (IDS). 
     SUMMARY 
     In accordance with one aspect, the present invention provides a method of examining a data stream to detect presence of at least one complex string belonging to a predefined complex dictionary. The method comprises steps of: associating an array of state variables and an array of reference states with the complex dictionary with one-to-one correspondence between entries of the complex dictionary, the array of state variables, and the array of reference states; detecting a simple string in the data stream, the simple string being a constituent simple string in each of at least one complex string in the complex dictionary; updating a state variable associated with the each of at least one complex string according to all relative positions of the simple string within the each of at least one complex string; and determining that the each of at least one complex string is present in the data stream when the state variable attains a corresponding reference state. 
     In one realization of the method, a multi-bit Boolean state variable is used. Consequently, the step of updating the state variable comprises steps of: (1) shifting bits of the Boolean state variable a number of positions determined according to a position of the simple string in the data stream and a previous position of any simple string detected in the data stream and belonging to the each of at least one complex string; (2) setting an end bit of the Boolean state to indicate logical TRUE; and (3) performing a logical AND of the state variable and a bitmask associated with the simple string, the bitmask indicating the all relative positions of the simple string within the each of at least one complex string. 
     The bitmask may originate at the rightmost bit and, consequently, the Boolean state variable is shifted in the direction from right to left with the rightmost bit of the Boolean variable set to equal TRUE. Alternatively, the bitmask may originate at the leftmost bit and, consequently, the Boolean state variable may be shifted in the direction from left to right with the leftmost bit of the Boolean variable set to equal TRUE. 
     The corresponding reference state indicates a specific relative position of a last character of a last simple string in each of the at least one complex string. In an exemplary realization, the corresponding reference state is a multi-bit Boolean constant having a bit corresponding to the specific relative position set to logical TRUE, and the presence of each of the at least one complex string in the data stream is determined by performing a logical AND of the state variable and the corresponding reference state, with the outcome overwriting a current value of the state variable. The bit in the state variable at a position corresponding to the specific relative position is then examined and if its state is TRUE, detection of a respective complex string may be ascertained subject to congruence of a suffix of the respective complex string to a corresponding portion of the data stream. 
     In accordance with another aspect, the present invention provides a method of detecting the presence of a selected complex string from a predefined complex dictionary in a data stream. The selected complex string comprises a predefined number χ&gt;1 of simple strings with each simple string having a prefix of indefinite characters. The last simple string of the complex string has a suffix of indefinite characters, which is considered a suffix of the complex string. The prefix of the first simple string may be a NULL string, and the suffix may be a NULL string. A NULL string is an empty string with zero characters. The method comprises steps of: locating a first portion of the data stream where the first portion is identical to a first simple string of the selected complex string; ascertaining congruence of an adjacent portion of the data stream, preceding the first portion, to a prefix of the first simple string; locating a second portion of the data stream where the second portion is identical to a second simple string of the selected complex string; and ascertaining congruence of an intervening portion between the first portion and the second portion to a prefix of the second simple string. Notably, congruence of a NULL string to any other NULL string is, by definition, ascertained. 
     The method includes a further step of ascertaining congruence of the suffix to a corresponding portion of the data stream determined according to a length of the complex string. Where the number of simple strings in a complex string exceeds 2, i.e., χ&gt;2, the method comprises further steps of locating an m th  portion of the data stream is the m th  portion being identical to an m th  simple string of the selected complex string; and ascertaining congruence of an intervening portion, of the data stream, preceding the m th  portion and a prefix of an m th  simple string, the intervening portion following an (m−1) th  portion of the data stream found to be identical to an (m−1) th  simple string in the selected complex string. 
     In accordance with another aspect, the present invention provides a method of identifying a complex string in a data stream, the method comprising steps of: segmenting the complex string into a suffix and a set of string segments, each string segment comprising a prefix and a simple string, where the prefix comprises indefinite characters and the suffix comprises indefinite characters; generating an array of bitmasks each bitmask associated with a string segment in the set of string segments, the bitmask indicating a location index of an end character of the each string segment; associating a Boolean state variable with the complex string; locating a current simple-matching position in the data stream at which at least one simple-string belonging to the complex string terminates; initializing a current mask as an opaque mask; performing a logical OR operation of the current mask with selected bitmasks corresponding to the at least one simple string to produce a composite current mask; determining a gap between the current simple-matching position and a previous simple-matching position; left-shifting each entry of the state variable by the gap, replacing each shifted entry by “0” and setting the rightmost entry to “1”; and updating the state variable according to a logical AND operation with the composite current mask. 
     The method comprises a step of determining that a portion of the data stream terminating at the current simple-matching position contains the complex string when a bit in a target position of the state variable represents a logical TRUE, where the target position corresponds to a last location index of an end character of a last string segment within the complex string. 
     The method comprises a further step of associating a segment descriptor with each string segment. The segment descriptor indicates a length of the string segment and a length of a prefix of the string segment. A last string segment within the complex string further comprises indications of a length of the suffix and a target position corresponding to a last location index of an end character of a last string segment within the complex string. Each selected bitmask associated with a specific simple string satisfies a condition of congruence of a prefix of the specific simple string and a corresponding portion of the data stream. When the suffix includes at least one character, the method comprises a step of determining that a portion of the data stream terminating at a position of index equal to an index of the current simple-matching position plus a length of the suffix when the state variable contains a logical TRUE at the target position and the suffix is congruent with a portion of the data stream succeeding the current simple-matching position. 
     In accordance with a further aspect, the present invention provides a method of screening a data stream to detect presence of any complex string from a predefined complex dictionary. The method comprises a preprocessing step of transforming the complex dictionary into a segmented dictionary, an array of segment descriptors, and an array of bitmasks. The segmented dictionary comprises string segments of each complex string in the complex dictionary, each string segment comprising a prefix and a simple string. Each segment descriptor defines a content of a corresponding string segment in the segmented dictionary, and each bitmask indicates a location of a string segment in the segmented dictionary within a respective complex string in the complex dictionary. Steps for detecting and locating complex strings in the data stream include: performing a simple search to produce detected simple strings in the data stream, where the simple strings are defined in the segmented dictionary; selecting candidate string segments from among specific string segments containing the detected simple strings; and identifying candidate complex strings for the at least one complex string in the complex dictionary, where the candidate complex strings contain the candidate string segments, using the array of segment descriptors and the array of bitmasks. 
     The method further comprises: (1) associating a state variable with each complex string in the complex dictionary; (2) updating the state variable according to gauged spans between successive positions in the data stream, at which positions simple strings belonging to the each complex string terminate; (3) determining, at each position, a subset of the candidate string segments belonging to the each complex string; and (4) correlating the state variable with locations, within the each complex string, of candidate string segments of the subset. The step of correlating further comprises formulating a composite current mask as a logical OR function of bitmasks of candidate string segments of the subset, and performing a logical AND operation of the composite current mask and the state variable to produce an updated state variable. The step of selecting comprises a further step of ascertaining congruence of a portion of the data stream preceding each detected simple string to a prefix of a corresponding string segment from among the specific string segments. 
     In an exemplary embodiment, the present invention provides an article of manufacture comprising at least one processor-readable medium and instructions carried on the at least one medium. The instructions are devised to cause a processor to transform the complex dictionary into a structure which enables computationally efficient search. The structure comprises a segmented dictionary, an array of segment descriptors, and an array of bitmasks. The segmented dictionary comprises string segments of each complex string in the complex dictionary, and a suffix. Each string segment comprises a prefix and a simple string. Each segment descriptor defines content of a corresponding string segment in the segmented dictionary. Each bitmask indicates a location of a string segment in the segmented dictionary said string segment being within a respective complex string in the complex dictionary. The instructions are further devised to cause the processor to perform simple search to produce detected simple strings defined in the segmented dictionary, select candidate string segments from among specific string segments containing the detected simple strings, and identify candidate complex strings in the complex dictionary containing the candidate string segments using the array of segment descriptors, and the array of bitmasks. The instructions further causes the processor to ascertain congruence of the prefix of each string segment with a corresponding portion of the data stream and congruence of the suffix of each complex string with a corresponding portion of the data stream. Where the prefix is a NULL prefix, having zero characters, and where the suffix is a NULL suffix, congruence is inherently ascertained. 
     In an alternate realization of the article of manufacture, the segmented dictionary comprises a prefix and string segments of each complex string in the complex dictionary, with each string segment comprising a simple string and a suffix. The instructions cause the processor to operate to ascertain congruence of the prefix with a respective portion of the data stream and congruence of the suffix of the each string segment with a corresponding portion of the data stream. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the present invention will be further described with reference to the accompanying exemplary drawings, in which: 
         FIG. 1  illustrates a prior-art system for matching each of a set of reference strings with potential corresponding strings in a text; 
         FIG. 2  illustrates an exemplary structure of a complex string for use in embodiments of the present application; 
         FIG. 3  illustrates exemplary indefinite characters in the complex string of  FIG. 2 ; 
         FIG. 4  illustrates alternate forms of segmented complex strings for use in embodiments of the present invention; 
         FIG. 5  illustrates a mechanism for detecting and locating complex strings in input data and communicating results to a decision module which determines a course of action, in accordance with an embodiment of the present invention; 
         FIG. 6  illustrates the main steps of a method of detecting complex strings in accordance with an embodiment of the present invention; 
         FIG. 7  illustrates a process of segmenting a complex dictionary into a dictionary of string segments with two associated segment descriptors and bitmasks to relate each string segment to its parent complex string, in accordance with an embodiment of the present invention; 
         FIG. 8  illustrates a mechanism for detecting reference complex strings in a data stream using the segmented dictionary structure of  FIG. 7 , the mechanism using a simple-search module, and a complex-string-identification module, in accordance with an embodiment of the present invention; 
         FIG. 9  illustrates an exemplary method implemented in the complex-string-identification module of the mechanism of  FIG. 8  in accordance with an embodiment of the present invention; 
         FIG. 10  illustrates details of a step of constructing a composite Boolean MASK in the method of  FIG. 9  in accordance with an embodiment of the present invention; 
         FIG. 11  illustrates details of a step of updating a Boolean STATE variable for determining search progress of a specific complex string in accordance with an embodiment of the present invention; 
         FIG. 12  illustrates steps, according to the method of  FIG. 9 , of detecting a target complex string in input data where the complex string is segmented according to the first segmentation form of  FIG. 4  and each of two consecutive string segments in the input data is compatible with the first string segment of the target complex string; 
         FIG. 13  illustrates steps of searching for a target complex string in input data which contains a complex string of close proximity to the target complex string, where the complex string is segmented according to the first segmentation form of  FIG. 4 ; 
         FIG. 14  illustrates steps of detecting a target complex string in input data where the complex string is segmented according to the first segmentation form of  FIG. 4  and two consecutive string segments in the input data have prefixes of different sizes but each of the corresponding simple strings is compatible with the first simple string of the target complex string; 
         FIG. 15  illustrates steps of detecting a target complex string in the same input data considered in  FIG. 12  but with the complex string segmented according to the second segmentation form of  FIG. 4 ; 
         FIG. 16  illustrates steps of detecting a target complex string in input data where the complex string is segmented according to the first segmentation form of  FIG. 4  and where the target complex string includes multiple equivalent string segments leading to a composite (comb) MASK, in accordance with an embodiment of the present invention; 
         FIG. 17  illustrates steps of detecting the target complex string considered in  FIG. 16  in input data which includes characters that are incongruent with corresponding prefix characters within the target complex string; 
         FIG. 18  illustrates a process of creating a composite mask in accordance with an embodiment of the present invention; 
         FIGS. 19-21  illustrate steps of detecting any of three target complex strings of a complex dictionary in a first input-data sample in accordance with an embodiment of the present invention; 
         FIGS. 22-23  illustrate steps of detecting any of three target complex strings of a complex dictionary in a second input-data sample in accordance with an embodiment of the present invention; 
         FIG. 24  illustrates the steps of  FIG. 19  using an equivalent alternate form of Boolean bitmasks in accordance with an embodiment of the present invention; 
         FIG. 25  illustrates an exemplary complex dictionary comprising complex strings where a pair of complex strings may contain identical simple strings for use with an embodiment of the present invention; 
         FIG. 26  illustrates a segmented dictionary and an associated segment-descriptor matrix derived from the complex dictionary of  FIG. 25  according to an embodiment of the present invention; 
         FIGS. 27 and 28  illustrate a bitmask-array comprising Boolean bitmasks each associated with a string segment in the segmented dictionary of  FIG. 26 ; and 
         FIG. 29  illustrates a position array each element of which containing a preceding input-data position for a corresponding complex string in the complex dictionary of  FIG. 26  and a STATE array each element of which being a Boolean variable of multiple bits indicating a search progress for a corresponding complex string in the complex dictionary of  FIG. 25  for use in an embodiment of the present invention. 
     
    
    
     TERMINOLOGY 
     Alphabet: The term alphabet refers to a set of characters which may include punctuation marks and spaces. 
     Class: A subset of characters may be selected to form an alphabet class. The selected subset of characters may be arranged in an arbitrary order. For brevity, the term “class” will be consistently used herein to refer to an alphabet class. Several classes may be formulated. 
     Indefinite character: An indefinite character is an ordinary character of the alphabet which derives the indefinite status from its position in a predefined string of characters. An indefinite character belongs to one of predefined classes and possibly to more than one class. One of the classes may encompass the entire alphabet, and a character belonging to such class is treated as a character with a “don&#39;t care” attribute. 
     Coherent word: A coherent word comprises a sequence of characters. It is a character-defined word in which each character is an ordinary character uniquely defined in the alphabet. If the alphabet includes punctuation marks and spaces, a group of coherent words may also be treated as a single coherent word. 
     Ambiguous word: An ambiguous word is a class-defined word in which each character is defined according to class association. 
     Simple string: A simple string comprises a coherent word. As described above, several coherent words separated by spaces may also constitute a single coherent word. 
     Ambiguous string: The term “ambiguous string” is herein used synonymously with the term “ambiguous word”. 
     Complex string: A complex string comprises at least two words, of which at least one word is an ambiguous word and at least one ambiguous word is subject to at least one restriction such as a predefined number of characters or membership of constituent characters in specific classes. 
     Prefix: An ambiguous word preceding a simple string within a complex string is called a prefix. 
     Suffix: An ambiguous word succeeding a simple string within a complex string is called a suffix. 
     String segment: A string segment may comprise a prefix and immediately following simple string or a simple string and an immediately following suffix. Either of the two definitions may be adopted as long as it is used consistently. 
     String equality: Two strings are said to be equal, or equivalent, if they are identical. 
     String congruence: Two strings are said to be congruent if they have the same number of characters and if likewise positioned characters in the two strings belong to the same class. This applies to a pair of ambiguous strings or to a pair of complex strings. 
     String matching: Two simple strings (coherent strings) are said to be matching strings if they are equal. Two complex strings are said to be matching strings if there is one-to-one equality of their constituent coherent strings and one-to-one congruence of their constituent ambiguous strings. 
     Simple dictionary: A simple dictionary may be devised to include a set of simple strings of special interest. The simple dictionary may expand or shrink as the need arises. 
     Complex dictionary: A complex dictionary comprises a set of complex strings. A simple string may be treated as a reduced complex string and, therefore, the method of the invention will focus on complex strings. The set of complex strings may be updated to add new complex strings or delete existing complex strings. 
     Text: A text is a sequence of characters extracted from a data stream and may include ordinary characters and indefinite characters. A text may be examined to ascertain the existence of any of complex string from among a predefined set of reference complex strings forming a complex dictionary. 
     Mask: A mask is a sequence of bits, each bit assuming either of the two states “false” or “true”. When a mask is ANDed with a first Boolean variable of equal length to produce a second Boolean variable, each bit of the second Boolean-variable at a position corresponding to a mask bit of false state (binary 0) is also of false state. Each bit of the second Boolean-variable at a position corresponding to a mask bit of true state (binary 1) has the same state of the corresponding bit of the first Boolean variable. 
     Opaque mask: A mask in which each bit represents logical FALSE, binary 0 for example, is an opaque mask. 
     State: The state of an n-bit Boolean state variable is indicated by bits set to represent logical TRUE (binary 1) and may be denoted {p 0 , p 1 , . . . , p n }, where p j , 0≦j&lt;n, are positions in the state variable each having a value of binary 1. For example, a 32-bit Boolean state variable having a value of [00000000001000010000000000000001] may be represented as {0,16,21}, with the rightmost bit being the origin of index 0. A state variable having a null value, where all its bits are set to binary 0, is denoted { }. 
     String Length: The length of any string is the number of characters of the string, including indefinite characters. 
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     The method of the present invention, which applies to complex strings, is devised to reduce memory consumption, minimize the computation effort, reduce computation-time variance, and present detected complex strings in the order in which they are encountered in an examined data stream. 
       FIG. 1  illustrates a conventional string-search mechanism  100  where a string locator  120  receives a text  160  and attempts to find portions of the text that are listed in a set  140  of reference strings. The output  180  of the string locator includes locations in the text of each found string. The location information may then be used to produce a variety of reports depending on the application. 
       FIG. 2  illustrates a portion of a text including two successive independent simple strings “Simple String1” and “Simple String2”, referenced as  210 - 1  and  210 - 2  respectively, which are found in the set  140  of reference strings. The string locator  120  identifies the two simple strings independently. The lengths and content of the preceding substring  212 , the intervening substring  214 , and the succeeding substring  216  are irrelevant. 
       FIG. 2  also illustrates a portion of a text which includes an exemplary complex string  220  that belongs to some complex dictionary. The complex string  220  includes three simple strings  230  with the content “One”, “Complex”, and “string”. The content of the simple strings  230 , together with the preceding, intervening, and succeeding substrings, collectively marked by successive occurrence of a virtual character ‘*’ (a space holder), determine whether the complex string  220  is congruent with one of reference complex strings in some predefined complex dictionary. The use of the symbol “*” in any position in complex string  220  should be understood to indicate that an indefinite character may occupy the position. The indefinite character may be any of a predefined subset of characters, such as subsets {A, a, B, b}, {0,1,2,3,4,5}, or {$, ^, *, +”}, the character * in the latter being an ACTUAL character *. Each indefinite character in complex strings  222  and  224  belongs to one of five classes defined in  FIG. 3 . A character belonging to class j, is identified as  240 - j,  0≦j&lt;5. 
     It is noted that a complex dictionary preferably includes only mutually distinct complex strings. However, as will be described below with reference to  FIG. 25 , the method of the invention is sufficiently flexible to accept a complex dictionary in which any of the reference complex strings may be replicated for whatever reason. It is further noted that a complex string may comprise multiple identical simple strings. A constituent simple string of any complex string may also be found in other complex strings in the same complex dictionary. 
     In one realization of complex string  220 , each character “*” may indicate a logical “don&#39;t care” (a term used extensively in the art). Accordingly, a character “*” may correspond to any recognizable character in a recognized alphabet-list. With 24 such characters in the exemplary complex string  220 , and considering a recognizable alphabet of 80 unique characters (comprising, for example, the upper-case and lower-case English characters, 10 single decimal digits, and 18 auxiliary symbols and punctuation marks), the number of simple strings that can be manufactured to be congruent with the exemplary complex string  220  is the astronomical 80 24 . Of course, considering grammatical constraints in both natural languages and computer-tailored languages, the number of likely encounters in a data stream of complex strings, congruent to the exemplary complex string  220 , may be reduced significantly. However, the number would still be too large to list the likely congruent strings in a simple dictionary adapted for use with a conventional simple-search method. 
     In general, individual indefinite characters “*” in complex string  220  may belong to different classes each class being defined by a corresponding subset of the alphabet. Two complex strings  222  and  224 , which may be encountered in a data stream contain identical simple strings in corresponding positions. The two complex strings, however, have different indefinite characters and the congruence, or otherwise, of the two strings is determined according to the class definition of the indefinite characters. 
       FIG. 3  illustrates an exemplary definition of five classes. The five classes are associated with class indicators 0 to 4. Class 0 encompasses all characters of the alphabet. Class 1 includes decimal digits 0 to 9. Class 2 includes upper-case characters A, B, C, and D. Class 3 includes upper-case characters U,V,W,X, and Y. Class 4 includes the symbols (herein also called characters) ^, #, $, ©, and &amp;. Many other classes may be defined. Based on the class definition of  FIG. 3 , the complex strings  222  and  224  of  FIG. 2  are determined to be congruent because each decimal digit in complex-string  222  corresponds to a decimal digit (not necessarily equal) in a corresponding position in complex-string  224 , each symbol of class 4 in complex-string  222  corresponds to a symbol of class 4 at a corresponding position in complex-string  224 ; and so on. 
     According to the method of the invention, a complex string is divided into string segments. By definition, a complex string contains a number of simple strings with intervening indefinite characters. The first constituent simple string may be preceded by indefinite characters, and the last constituent simple string may be succeeded by indefinite characters. The indefinite strings preceding a simple string is referenced as a “prefix” and the indefinite strings succeeding a simple string is referenced as a suffix. A prefix may have an arbitrary number, including zero, of characters. Likewise, a suffix may have an arbitrary number, including zero, of characters. A string segment may be defined as a concatenation of a prefix and a succeeding simple string or a concatenation of a simple string and succeeding suffix. 
       FIG. 4  illustrates two schemes for segmentation of a complex string to facilitate further processing. In the first scheme, the complex string is divided into string segments  420  each comprising a prefix  422  and a simple string  424 . A prefix may be a NULL prefix. In the second scheme, the complex string is divided into string segments  430 , each comprising a simple string  424  followed by a suffix  426 . A suffix may be a NULL suffix. According to the first scheme, string segments  420 , individually identified as  420 - 0 ,  420 - 1 ,  420 - 2 , and  420 - 3  are followed by a suffix  426 . According to the second scheme, prefix  422  is followed by string-segments  430 , individually identified as  430 - 0 ,  430 - 1 ,  430 - 2 , and  430 - 3 . Either of the two schemes may be used, as long as the same scheme is used consistently. 
       FIG. 5  illustrates a mechanism  530 , in accordance with an embodiment of the present invention, for detecting and locating any complex string belonging to a basic complex dictionary  520  in received input data  570  of a specific data stream. The mechanism  530  comprises a preprocessing module  524  for dividing each complex string into string segments according to either of the two segmentation schemes of  FIG. 4 . The segmented complex strings, together with other associated data are stored in a memory  526 . The preprocessing module  524  is activated only in response to changes in the basic complex dictionary  520 . The changes may include deletion or addition of reference complex strings. 
     A string-search module  528  receives input data  570  in data units and uses the segmented complex strings together with their associated data stored in memory  526  to determine the occurrence, or otherwise, of any of the complex strings of the basic complex dictionary  520  in the input data. When the occurrence of a complex string is determined, the position of the found complex string in the input data  570  is submitted to a decision module  580  which may take some corrective actions such as deleting the complex string from the input data  570  to produce a processed text  590 , or simply identifying the detected complex string in the processed text  590 . The string-search module  528  is a time-critical component of the mechanism  530  and, therefore, optimizing the string-search process is of paramount importance. However, even if the execution time is rendered negligibly small, a block of the input data  570  need be retained for possible modification if a specific reference complex string is found in the input data. The retained data block, which may comprise multiple data packets, a single data packet, or a fraction of a packet, is held in a buffer  578 . An upper bound of the size of a held data block, and hence a required storage capacity of buffer  578 , depends largely on the method of search. 
       FIG. 6  illustrates an overview of the method of the present invention. Initially, a state variable and a corresponding reference state are associated with each complex string in the basic complex dictionary  520 . In step  620 , a simple search detects a simple string in an examined data stream. The detected simple string may be one of a number of simple strings detected at a specific position in the data stream. The detected simple strings may belong to more than one complex string of the basic complex dictionary, and more than one detected simple string may belong to one complex string. Detected simple strings belonging to a specific complex string may be considered individual or collectively in the process of determining whether the specific complex string is present in the data stream. 
     Considering one detected simple string at a time, step  630  identifies all complex strings in the complex dictionary, which contain the simple string. Step  630  may employ any of well-established simple-search methods, such as the Aho-Corasick method. Up to this point, each of the identified complex strings is treated as a candidate complex string. In step  640 , the state variable associated with each candidate complex string is updated according to successive positions, in the data stream, at which any simple string belonging to the candidate complex strings is detected. In step  650 , the updated state variable of each candidate complex string is compared with a corresponding reference state to determine the existence, or otherwise, of the candidate complex string in the data stream. Step  660  examines the results of the comparison for each candidate complex strings individually. If detection is ascertained for an individual candidate complex string, step  670  indicates detection of the candidate complex string then determines its location in the data stream and reports all relevant information to the decision module  580 . The process then proceeds to step  620 . If detection of the individual complex string is not yet determined, step  660  directs the process to step  620 . Preferable, the execution of step  620  is performed after all candidate complex strings are examined in step  650 . 
       FIG. 7  illustrates further details of the segmentation process of the basic complex dictionary  520 . The preprocessing module  524  produces a segmented dictionary  750 , a set of segment descriptors  752 , and a bitmask array  754 . The segmented dictionary  750  includes either string-segments  420  for each complex string, followed by a suffix  426  or a prefix  422  followed by string-segments  430  ( FIG. 4 ). The segmented dictionary  750 , the set of segment descriptors  752 , and the bitmask array  754  may be held in separate memory devices or may share a common memory device. 
     A segment descriptor associated with each string segment  420  or  430  defines the composition of the string segment. If the first segmentation scheme of  FIG. 4  is used, a segment descriptor indicates lengths of the prefix and simple string of a string segment and the length of the suffix  426 . If the second segmentation scheme is used, a segment descriptor indicates the length of the prefix  422  and the lengths of a simple string and its suffix. 
     A bitmask is also associated with each string segment  420  or  430  in order to bind the string segment to its parent complex string. 
       FIG. 8  details the string-search module  528  which comprises a simple-search module  820  and a complex-string-identifier module  840 . The simple-search module  820  receives data units belonging to a data stream  812 , detects the occurrence of any of the simple strings in the segmented dictionary  750 , and determines the position of each detected simple string in the data stream. Any of prior-art methods of simple search, such as trie-based search methods, may be used in module  820 . Module  820  locates any detected simple string in the input data and communicates corresponding indices (pointers)  838  to the complex-string-identifier module  840 . Such indices serve only as intermediate indices which may be used in locating corresponding indices  848  for locating a complex string, if any, in the data stream  812 . The complex-string-identifier module  840  relates each simple-string index it receives from the simple-search module to: (1) a corresponding string-segment in the segmented dictionary  750 ; (2) a corresponding segment descriptor in the set  752  of segment descriptors; and (3) a corresponding bitmask in bitmask array  754 . Complex-string-identifier module  840  maintains a STATE array each element of which being a Boolean STATE variable for a corresponding complex string in the basic complex dictionary  520 . Each Boolean STATE variable contains a predefined number of bits; 64 for example. The complex-string identifier comprises software instructions for implementing a search method described below with reference to  FIGS. 9-11 . 
     In a preferred embodiment of the present invention, an Aho-Corasick automaton is created and used in the simple-search module  820 . The Aho-Corasick method detects simple strings in the order of their occurrence in the input data stream. The method also detects all overlapping simple strings that end at a single position in the data stream. Such overlapping simple strings would have at least one common end character. For example the two simple-strings chief and editor-in-chief would be reported if the simple-string editor-in-chief is encountered in the data stream  812  and if the two simple strings are placed in separate entries in a corresponding simple dictionary. The main desirable properties of a real-time string-search method include efficient memory utilization, predictable computation effort, and orderly listing where strings are detected in the order in which they occur in an examined data stream. Notably, the Aho-Corasick method, which is applicable to detection of simple strings, possesses such properties and is therefore a preferred method for incorporation in the simple-search module  820 . 
       FIG. 9  illustrates the main steps of the search method implemented in the complex-string identifier  840 . In step  920 , a matching position, p, of at least one simple string belonging to the segmented dictionary  750  is received. There may be a set Σ containing several simple strings ending at position p and all belonging to the segmented dictionary  750 . The simple strings in set Σ may belong to more than one complex string of the basic complex dictionary  520 . The set Σ is then divided in step  922  into subsets of simple strings with each subset including simple strings belonging to only one complex string in the basic complex dictionary  520 . In step  924 , one of the subsets, associated with a specific complex string C is selected. In step  926 , an intermediate Boolean variable MASK is created using bitmasks in bitmask array  754  corresponding to the subset of simple strings selected in step  924 . The value of the Boolean variable MASK is initialized as an opaque mask where each bit is set to “false”, which may be represented by logical ‘0’, at the start of each step  924 . The MASK is then modified under the condition of congruence of prefixes (or suffixes if the second scheme of  FIG. 4  is used) of the simple strings of the subset and corresponding prefixes (or suffixes) in the specific complex-string C. 
     In step  928 , the intermediate Boolean variable MASK is used to update the Boolean variable STATE in a STATE-array memory  860 . 
     In step  930 , the index, κ, of the last simple string in the specific complex string C, is selected from segment-descriptor set  752 , and the bit in Boolean variable STATE in position κ is examined. If the value of the bit is “false” (logical “0”), it is determined that the portion of the input data terminating in position p does not contain the specific complex string C and step  940  is then executed. If the value of the bit is “true” (logical “1”), it is then determined that the portion of the input data terminating in position p contains all the string segments  420  of the complex string C, and the occurrence of the entire complex string C in the input data is then decided in step  934  according to the co indefinite characters of the suffix of complex string C. If ω=0, indicating a NULL suffix, an occurrence of complex string C is ascertained and step  934  reports, to the decision module  580 , an occurrence of the specific complex string C in the portion of the input data terminating in position p. If ω&gt;0 and the suffix is incongruent with corresponding characters spanning positions (p+1) to (p+ω), it is determined in step  934  that the input data received so far does not contained the specific complex string C. Otherwise, step  934  reports, to the decision module  580 , an occurrence of the specific complex string C in the portion of the input data terminating in position (p+ω) and step  940  is executed next. 
     Step  940  determines if all strings in set Σ have been processed. If the set Σ is not yet exhausted, another subset is processed (step  924 ). Otherwise, a new simple-string matching position p, as determined in step  920 , is considered. 
       FIG. 10  details step  926  of  FIG. 9 . In step  1012 , a multi-bit Boolean variable MASK is initialized as an opaque mask, i.e., each bit of the Boolean variable MASK is initialized to logical “0”. The MASK is associated with a subset σ of simple-strings in Σ belonging to a single complex string. In step  1020 , a simple string, denoted S, is selected from the subset σ. The prefix, denoted X, of S in the specific complex string C is determined in step  1022  and compared with the prefix Y of S in a corresponding data segment in the segmented dictionary  750 . In step  1024 , if the prefix X and prefix Y are determined to be congruent, step  1026  updates the MASK by performing a bit-wise OR of the MASK and the bitmask associated with simple string S in bitmasks memory  754 , and step  1028  is executed next. The bitwise OR function implemented in step  1026  is denoted by the symbol “|”. Thus the operation: MASK|bitmask-of-S comprises logical OR operations for corresponding bits in the Boolean variable “MASK” and the Boolean constant “bitmask-of-S”. If the prefix X and prefix Y are incongruent, step  1024  leads directly to step  1028 . In step  1028 , if S is determined to be the last string in the subset a, step  926  is considered complete and the new value of MASK is ready for use in step  928  of  FIG. 9 . 
       FIG. 11  details steps  928  and  934  of  FIG. 9 . Step  928  updates the Boolean variable STATE associated with complex string C according to the set Σ of simple strings determined to terminate in position p of the input data. The previous position in the input data at which simple strings belonging to complex string C were detected is denoted π. Thus, after execution of step  928 , the present value of p overwrites the value of π for use in a subsequent execution of step  928  related to the same complex string C. Either of two schemes for identifying current positions p and previous positions π may be adopted. In a first scheme, both p and π may take cyclical values based on the length (number of bits) assigned to a bitmask (and hence to a state variable). In a second scheme, the values of p and π may be represented according to the word length of the computing platform. For example, with a word length of four bytes, p or π may assume a value between 0 and 4,294,967,295. With the search process continuing indefinitely, the values of p and π are still cyclic requiring a modulo process. However, the modulo process is used at a much lower rate. 
     The span between a current position p and a previous position π associated with a specific complex string is determined as [p−π] modulo Γ , Γ being determined according to either of the two schemes described above. In step  1120 , the Boolean variable STATE is shifted to the left a number of positions equal to the span associated with the complex string C. Each position in STATE, from which a bit is shifted, is assigned a value of “0”, except the right-most position which is always assigned a value of “1” after a shift operation. In step  1122 , a bit-wise logical ANDing is performed and the result overwrites the variable STATE. 
     As described, the bitmask used in step  1122  is considered to originate at the rightmost bit and, consequently, the Boolean state variable is shifted a number of bits equal to (p−π) modulo Γ , where Γ equals 2 W , W being the word length assigned to the position indices p and π, in the direction from right to left with the rightmost bit of the Boolean variable set to equal TRUE (binary 1). Alternatively, the bitmask may originate at the leftmost bit and, consequently, the Boolean state variable may be shifted in the direction from left to right with the leftmost bit of the Boolean variable set to equal TRUE as illustrated in  FIG. 24 . 
     In an alternate realization of the mechanism illustrated in  FIGS. 7-9 , the complex string may be segmented according to the second segmentation form of  FIG. 4  and the segmented dictionary may comprise a prefix and a number of string segments for each complex string in the complex dictionary, with each string segment comprising a simple string and suffix. Step  1022  would then be replaced with a step of determining a suffix of simple string S. Step  934  which determines congruence of a suffix of a complex string and a respective portion of input data would be replaced with a step of determining congruence of a prefix of the complex string and a respective portion of input data.  FIG. 15  illustrates steps of detecting a target complex string based on using the second segmentation form of  FIG. 4 . 
       FIG. 11  also details step  934 . Having determined, in step  932 , that the bit in position κ of the Boolean STATE variable equals a binary 1, it remains to ascertain the congruence of the suffix, if any, and a corresponding portion of the input data. In step  1152 , a length ω of the suffix of complex string C is read from the set of segment descriptors  752 . If ω is found to be zero, step  1154  directs the search process to step  1160  to report matching of the complex string C at position p. If ω&gt;0, step  1156  determines whether the ω indefinite characters of the suffix are congruent to input data characters spanning positions (p+1) to (p+ω). If congruence is ascertained, step  1158  directs the search process to step  1160  to report success in detecting complex string C in a portion of the input data ending at position (p+ω). If the congruence conditions are not met, step  1158  directs the search process to step  940  to either complete the examination of a current set Σ or consider a new matching position p. 
     Exemplary Execution of the Method 
       FIG. 12  illustrates steps of detecting an exemplary target complex string  1220  in input data  1250  of a data stream. The complex string  1220  is segmented according to the first segmentation form of  FIG. 4 . The complex string  1220  comprises three simple strings “DE”, “KL”, and “MPQST” having prefixes of length 4, 4, and 5, respectively. With the last simple string having a suffix of length 2, the total length of the complex string  1220  is 24 characters. The prefixes and the suffix comprise indefinite characters, each indefinite character being marked as “*”. The input data comprises two consecutive strings “DE” which are compatible with the first simple string the target complex string  1220 . 
     A ruler  1202  is used to indicate a position of each character of the input data  1250  and each character of the complex string  1220 . The input data extracted from a data stream may continue ad infinitum and, therefore, a position in the input data is indexed as a cyclic number. The ruler  1202  is a cyclic ruler having a range dictated by a number of factors including the hardware platform on which the method is realized into an article of manufacture. 
     In general, the simple search module  820  may detect several simple strings ending in one position of the input data.  FIG. 12 , however, illustrates a case where the simple-search module  820  detects only one simple string at each of current detection positions 06, 12, 18, and 28. A case with multiple simple-string detection is illustrated in  FIG. 19 . The gap δ between successive detection positions p is indicated in  FIG. 12 . Notably, the preceding detection position to current detection point p=06 is either 0 when the search mechanism is initialized, or known from a previous detection of a simple string belonging to the same exemplary complex string  1220 . 
     As described earlier, the preprocessing module  524  produces an array of bitmasks  754 , each bitmask indicating the relative positions of each simple string within its parent complex string. Three bitmasks  1240 , individually identified as  1240 - 0 ,  1240 - 1 , and  1240 - 2 , respectively indicate the relative positions of simple strings “DE”, “KL”, and “MPQST” in complex string  1220 . A Boolean state variable  1260  having 32 bits is associated with complex string  1220 . A current MASK is created in step  926  which is further detailed in  FIG. 9 . A bit in a bitmask  1240  set to logical FALSE (binary 0) is represented by a blank cell  1241 , and a bit set to logical True (binary 1) is represented by a hatched cell  1242 . Likewise a bit in state-variable  1260  set to logical FALSE is represented by a blank cell  1261  and a bit set to logical TRUE (binary 1) is represented by a hatched cell  1262 . A similar representation is used in  FIGS. 13-24 . 
     The current MASK is an outcome of bitwise OR operations of bitmasks of all simple strings detected at a given position in the input data  1250  subject to congruence of a prefix of each of simple strings to a corresponding portion of the input data as indicated in step  1022 . Notably, the state variable  1260  is initialized in step  1012  as an opaque mask in the process of creating a current mask detailed in  FIG. 10 . In the example of  FIG. 12 , it is assumed that the congruence condition is always satisfied and, because there is only one simple string detected at each of the four positions indicated, the current mask at each of the four detected positions (p=06, 12, 18, and 28) is equal to the bitmask in bitmask array  754  of the corresponding detected simple string. 
     As indicated in step  1120 , the state variable  1260  is shifted (p−π) bits (modulo Λ) and the rightmost bit of the shifted state variable is set to equal logical TRUE, which is equated to binary 1. With π=0 at position p=06, and starting with an opaque state { }, the state variable is shifted 6 bits to the left with the bit in position 0 set to equal binary 1 to attain a state of {0}. The shifted state variable is bitwise ANDed with bitmask  1240 - 0  corresponding to simple string “DE”. The result is a state of {0}, i.e., the rightmost bit of the state variable is set to binary 1 and each other bit is set to binary 0. There are two states corresponding to each detected simple string in the input data  1250 ; a first state resulting from executing step  1120  and a second state resulting from executing step  1122  of  FIG. 11 . At position p=12, the state variable is shifted (12−6) bits with the rightmost bit set to true to yield a state of {0,6}. The state variable  1260  is ANDed with bitmask  1240 - 0  corresponding to simple string “DE” and the result is a state {0}. At position p=18, the state variable is shifted (18−12) bits and the rightmost bit is set to binary 1 leading to state {0,6} again. The state variable  1260  is ANDed with bitmask  1240 - 1  corresponding to simple string “KL” to yield a state of {6}. At p=28, the state variable  1260  is shifted (28−18) bits with the rightmost bit set to binary 1 leading to state {0,16}. The state variable  1260  is then ANDed with bitmask  1240 - 2  corresponding to simple string “MPQST” to yield a state of {16}, which is the reference state of complex string  1220 . It remains to determine if the suffix of the complex string  1220  is congruent to the two characters succeeding the last simple string “MPQST”. Step  1152  of  FIG. 11  determines that the suffix of complex string  1220  is of length 2 characters and step  1156  ascertains congruence of the suffix (occupying positions 22 and 23 of complex string  1220 ) is congruent with the portion of the input data  1250  occupying positions 29 and 30, and step  1160  reports the presence of complex string  1220  in the input data  1250  starting at position 9 and ending in position 30. The bitmasks  1240  and the state variable  1260  are indexed in an ascending order from right to left, with the rightmost bit of each assigned an index of zero. A reverse ruler  1204  is therefore provided in  FIG. 12  and in subsequent figures. 
       FIG. 13  illustrates a search for the same target complex string  1220  of  FIG. 12  in input data  1350 , which differ slightly from input data  1250 , following the steps described above. The bitmasks  1240  in  FIGS. 12 and 13  are identical. Like state variable  1260 , state variable  1360  attains the states {0}, (0), {0,6}, 0}, {0,6} after processing the second simple string “DE”. However, because the simple string “KL” appears one-character earlier in input data  1350  in comparison with input data  1250 , the last state {0,6} is followed by state {6} (instead of corresponding {0,6} of  FIG. 12 ), leading to a subsequent opaque state { } after processing the simple string “KL” (compared to corresponding state {6} in  FIG. 12 ). The subsequent states attained when position p=28 is encountered are {0,11} which yields the opaque state { } when ANDed with bitmask  1240 - 2 . At this point, step  932  of  FIG. 9  directs the process to step  940  to start the search for a simple string, in the input data, that belongs to the complex string  1220 . 
       FIG. 14  illustrates a search for the same target complex string  1220  of  FIG. 12  in input data  1450  which differs slightly from input data  1250 . The simple strings “DE”, “DE”, “KL”, and “MPQST” in input data  1450  occupy positions p=6, 13, 19, and 29 compared to  6 ,  12 ,  18 , and  28  in input data  1250 . The first detected simple string “DE” is irrelevant in the examples of  FIGS. 12 and 14 . The effect of the one-character shift is that the state variable  1460  acquires states {0}, {0}, {0,7}, {0}, etc., instead of states {0}, {0}, {0,6}, {0}, etc. of state variable  1260 , and the complex string  1220  is determined to occupy positions 10 to 31 of the current cycle of input data  1450 . 
       FIG. 15  illustrates the detection of complex string  1220  in input data  1250  using similar steps to those of  FIG. 12  except that the complex string  1220  is segmented according to the second segmentation form of  FIG. 4 . The bitmasks  1540 - 0 ,  1540 - 1 , and  1540 - 2  for simple strings “DE”, “KL”, and “MPQST”, respectively, of the complex string  1220  are simple bitmasks each having a single bit set to binary 1 as illustrated by hatched cells  1542  in  FIG. 15 . A blank cell  1541  represents binary 0. Simple strings “DE”, “DE”, “KL”, and “MPQST” are detected at positions q=5, 11, 17, and 24. Starting with an opaque state { }, the state variable  1560  assumes states {0}, {0}, at position q=5, {0,6}, {0}, at position q=11, {0,6}, {6}, at position q=17, and {0,13}, {13}, at q=24. The last state {13} is in agreement with the bitmask  1540 - 2  of the last simple string “MPQST” of complex string  1220 . If congruence of the prefix of the first string “DE” in complex string  1220  with corresponding characters occupying positions 7, 8, 9, and 10 in input data  1250  is ascertained, the portion of input data  1250  occupying positions 7 to 30 is considered to include the entire complex string  1220 . 
       FIG. 16  illustrates steps of detecting a target complex string  1620  in input data  1650  where the complex string  1620  includes multiple congruent string segments each including a prefix of two characters and the simple string “DE”. As described earlier, the purpose of a bitmask associate with a simple string is to relate the simple string to its parent complex string. When a simple string “DE” is detected in input data  1650 , means for considering all occurrences of “DE” in the complex string  1620  need be provided. In accordance with the method of the present invention, a composite (comb) bitmask  1680 - 0  is devised in step  926  of  FIG. 9  (further detailed in  FIG. 10 ). Subject to congruence conditions of step  1022 , the composite bitmask  1680 - 0  includes a bit set to binary 1 (logical TRUE) at positions 0, 4, and 8 where binary 1 at position 0 corresponds to the position of the end character of the first occurrence of “DE”, and the binary 1 in positions 4 and 8 correspond to the end characters of the second and third occurrences of “DE” in the complex string. Bitmasks  1640 - 1  and  1640 - 2 , for simple strings “KL” and “MPQST” respectively, are simple bit masks; each includes only one bit set to binary 1. The process of determining the presence, or otherwise, of complex string  1620  in input data  1650  proceeds as described in  FIGS. 9 to 11 , and as further illustrated in the example of  FIG. 12 . It is noted that the input data includes an additional simple string “DE” which is detected by the simple-search module  820  and automatically filtered out. For each position p where at least one simple string is detected in the input data  1650 , the state variable  1660  is updated in step  1120  then in step  1122  illustrated in  FIG. 11 . Starting with the opaque state { }, the state variable  1660  successively attains the states {0), {0}, {0,4}, {0,4}, {0,4,8}, {0,4,8}, {0,4,8,12}, {0,4,8}, {0,6,10,14}, {14}, {0,22}, and {22} corresponding to positions p=4,8,12,16,22, and 30, respectively. It is noted that there are two states corresponding to each detected simple string in the input data  1650 ; a first state resulting from step  1120  and a second state resulting from step  1122 . Successful detection is ascertained when the last state of state variable  1660  attains the value of {22} which is the reference state for the complex string  1620 . 
       FIG. 17  illustrates the case of  FIG. 16  but with characters preceding simple string “KL” in input data  1750  associated with classes that are different from classes of their counterpart characters in the prefix of simple string “KL” in the target complex string  1620 . This results in step  926  ( FIGS. 9 and 10 ) yielding an opaque mask for p=22 which when ANDed with the current value of the Boolean state variable  1760  yields an opaque state variable, which in effect erases the state information acquired so far. The subsequent state of the state variable  1760  at position p=30 is then {0, 8} which does not include the target state {22}. The deviation of the state  1760  from its counterpart state  1660  is indicated in  FIG. 17  by the mark “x” in state variable  1760  corresponding to p=22. 
       FIG. 18  illustrates the execution of step  926  of the method of  FIG. 9 , which is further detailed in  FIG. 10 . The target complex string  1820  includes simple strings “ABCD”, “CD”, “D”, “CD”, and “BCD”. At position 7 of input data  1850 , the simple search module  820  detects the five simple strings  1825  in proper order as indicated. The bitmasks ( 1840 ) for the five simple strings  1825  yield a composite mask  1880 A if all the congruence conditions of step  1022  are met. A composite mask  1880 B results if the indefinite character ‘f’ preceding the simple string “CD” ending in position 19 of the input data is incongruent with the prefix character of position 14 of the target complex string  1820 . 
       FIG. 19  illustrates a set of reference complex strings  1920 - 0 ,  1920 - 1 , and  1920 - 2  and input data  1950  of a data stream comprising simple strings belonging to the set of reference complex strings  1920 . The first reference complex string  1920 - 0  contains simple strings “UVWXY”, and “ABCD”. The second reference complex string  1920 - 1  contains simple strings “ABCD”, “CD”, and “CD. The third reference complex string  1920 - 2  contains simple strings “DC”, “CD”, and “CD”. Each indefinite character in the reference complex strings is identified by a symbol “*”. Individually, the indefinite characters may belong to different classes despite the common identification “*”. The segmented dictionary  750  includes eight simple strings “UVWXY”, “ABCD”, “BCD”, “CD”, “CD”, “DC”, “CD”, and “CD”. The simple-search module  820  examines the input data to detect simple strings belonging to the segmented dictionary  750 . 
     At position 7 (according to ruler  1202 ) of the input data  1950 , the simple-search module  820  detects a set Σ (0)  of six simple strings “ABCD”, “BCD”, “CD”, “CD”, “CD”, “CD”, out of the eight simple strings of the segmented dictionary  750 , and associates each of the detected simple strings with a parent complex string. A subset σ 0  of Σ (0) , referenced as  1925 - 0 , contains detected simple strings (only one in this example) belonging to complex string  1920 - 0 . A subset σ 1  of Σ (0) , referenced as  1925 - 1 , contains detected simple strings (three in this example) belonging to complex-string  1920 - 1 . A subset σ 2  of Σ (0) , referenced as  1925 - 2 , contains detected simple strings (two in this example) belonging to complex-string  1920 - 2 . The simple string “CD” further appears separately in two portions of input data  1950  to be detected later by the simple-search module  820 . Each of the simple strings in set Σ (0)  belongs to at least one string segment in at least one complex string in the set of reference complex strings  1920 . String segments in the set of reference complex strings  1920  are candidate string segments. Their presence in the input data  1950  may be ascertained only after satisfying congruence conditions as described earlier with reference to  FIG. 10  (step  1022 ). The set of reference complex strings  1920  represents a basic complex dictionary  520  containing only three complex strings. In general, a basic complex dictionary  520  may comprise a significantly larger number of complex strings, and detected simple strings such as those of subset σ 1  of Σ (0)  may belong to many candidate string segments in segmented dictionary  750  ( FIG. 7 ) which, in turn, may belong to many candidate complex strings in the basic complex dictionary. Each candidate string segment is considered for further processing only after ascertaining congruence of its indefinite characters and corresponding characters of the input data. 
     Assuming congruence of all the indefinite characters in the reference complex strings  1920  to corresponding characters of input data  1950 , based on the prefix and suffix definitions, the current masks corresponding to subsets σ 0 , σ 1 , σ 2  of Σ (0)  are determined according to the bitwise OR operation of step  1026 . Thus, the bitmask for subset σ 0  of Σ (0)  has only one bit in position 9 set to binary 1. The position of the set bit corresponds to the displacement (19−10) of the end character “D” of the detected simple string “ABCD” from the end character “Y” of the first simple string “UVWXY” of complex-string  1920 - 0 . The bitmask for subset σ 1  of Σ (0)  has three bits in positions 0,4, and 12 set to binary 1, the positions being determined by the displacement of each of the simple strings in σ 1  from the end character “D” of the first simple string “BCD” in complex string  1920 - 1 . The bitmask for subset σ 2  of Σ (0)  has two bits in positions 10 and 17 set to binary 1, the positions being determined by the displacement of each of the simple strings in σ 2  from the end character “C” of the first simple string “DC” in complex string  1920 - 2 . 
     As illustrated in  FIG. 20 , the simple-search module  820  detects a set Σ (1)  of three simple strings “CD”, “CD”, and “CD” at position 11 (according to ruler  1202 ) of the input data  1950  with a subset  2025 - 1  having one simple string belonging to complex-string  1920 - 1 , and a subset  2025 - 2  having two simple strings belonging to complex string  1920 - 2 . With congruent conditions for all suffix and prefixes of each complex string  1920  satisfied, the composite current mask  2080 - 1  for subset  2025 - 1  has bits set to binary 1 in positions 4 and 12, determined as the displacements (8−4) and (16−4). The composite current mask  2080 - 2  for subset  2025 - 2  is the same as composite current mask  1980 - 2 . 
     At position 19 (according to ruler  1202 ) of the input data  1950 , the simple-search module  820  detects a set Σ (2)  identical to Σ (0)  and the same composite current masks  2080 - 1  and  2080 - 2  also apply. 
       FIG. 21  illustrates the outcome of step  928  which updates the states of state variable  2160  associated with complex string  1920 - 1  (“**BCD**CD******CD******”). Complex-string  1920 - 1  is the only one of complex strings  1920  that is present in the input data  1950 . Starting from the null state { }, and following the state transitions effected by step  928  ( FIG. 9  and  FIG. 11 ), the successive states of state variable  2160  are {0}, {0}, {0, 4}, {4}, {0, 12}, and {12}, which are identified in  FIG. 21  with references  2160   a   0 ,  2160   b   0 ,  2160   a   2 ,  2160   b   2 . States  2160   aj  and  2160   bj , where j=0, 1, or 2, result from execution of steps  1120  and  1122 , respectively, of  FIG. 11 . The last state {12} equals the reference state of complex-string  1980 - 1  which is determined as the displacement of the last character of the last simple string “CD” from the last character of the first simple string “BCD”. 
       FIGS. 22-23  illustrate a case where the reference complex strings are the same as those of  FIG. 19 , but the input data  2250  differs only in position 5 (according to the same ruler  1202 ) where character “B” is replaced with “Q”. This results in the absence of simple strings “ABCD” and “BCD” from the set Σ (0)  and, consequently, a transition from state { }, of state variable  2360  associated with reference complex string  1920 - 1 , to states {0}, then the opaque state { } to indicate absence from the input data  2250  of the first simple string “BCD” of complex string  1920 - 1 , with a final opaque state { }. Thus, starting from the null state { }, and following the state transitions effected by step  928  ( FIG. 9  and  FIG. 11 ), the successive states of state variable  2360  are {0}, { }, {0}, { }, {0}, and { }, which are identified in  FIG. 23  with references  2360   a   0 ,  2360   b   0 ,  2360   a   2 ,  2360   b   2 . States  2360   aj  and  2360   bj , where j=0, 1, or 2, result from execution of steps  1120  and  1122 , respectively, of  FIG. 11 . 
       FIG. 24  illustrates the detection process of  FIG. 22  with the bitmasks and Boolean state variables each having the leftmost bit, instead of the rightmost bit, as the origin with index 0. The set of composite current masks  2480 - 0 ,  2480 - 1 , and  2480 - 2  of  FIG. 24  is a mirror image of the set of composite current masks  2180 - 0 ,  2180 - 1 , and  2180 - 2  of  FIG. 21 . The Boolean state variable  2460  of  FIG. 24  is a mirror image of the Boolean state variable  2160  of  FIG. 21 . 
       FIG. 25  illustrates an exemplary basic complex dictionary  520  ( FIG. 5 ) comprising 16 complex strings  2510 - 0 ,  2510 - 1 , . . . ,  2510 - 15 , each having simple strings  2520 . Successive simple strings  2520  are separated by ambiguous words. Each of complex strings  2510 - 6  and  2510 - 11  has a prefix  2522  and each of the remaining complex strings  2510  has a null prefix. Each of complex strings  2510 - 4 ,  2510 - 5 ,  2510 - 6 ,  2510 - 8 , and  2510 - 11  has a null suffix and each of the remaining complex strings  2510  has a suffix  2524 . 
     The 16 complex strings  2510  are distinct. However, the method described with reference to  FIGS. 9-11  tolerates repeated complex strings  2510  in the complex dictionary  520 . Several constituent simple strings  2520  are common in more than one complex string  2510 . For example, the simple-string “Wilkinson” is common in complex strings  2520 - 1 ,  2520 - 4 ,  2520 - 5 ,  2520 - 6 ,  2520 - 14 , and  2520 - 15 . 
       FIG. 26  illustrates the process of segmenting complex dictionary  520  into a segmented dictionary  2650  and a segment-descriptor matrix  2652 . Each entry in the segmented dictionary  2650  includes a string segment comprising a prefix  2622  (which can be a null prefix) and one simple string  2620 . A last string segment of each complex string has an appended suffix  2624 , which can be a null suffix. Each row in segment-descriptor matrix  2652  includes a field  2612  indicating a length of a prefix (which may be zero) and a field  2614  indicating a length of the corresponding string segment (which includes the length of the simple string of the string segment plus the length of its prefix). A row in segment-descriptor matrix  2652  corresponding to a last segment of a complex string further includes a field  2616  indicating a length of a suffix (which may be zero) and a field  2618  indicating a sum of lengths of string segments, excluding the first string segment, of a corresponding complex string. The content of field  2618  defines a corresponding bitmask. 
       FIGS. 27-28  illustrate a bitmask array  2754 , comprising bitmasks  2740  for relating each string segment to its parent complex string. The bitmasks are of equal length. Examples of bitmasks  2740  are presented in  FIGS. 12-16  where they are referenced as  1240  in  FIGS. 12-14, 1540  in  FIG. 15, and 1640  in  FIG. 16 .  FIGS. 27-28  illustrate bitmasks in their initial state, each being initialized as an opaque mask represented as a sequence of binary “0”. To facilitate observation of state change, the bitmasks and the Boolean state variables in  FIGS. 12-24  are illustrated as sequences of blank and hatched cells instead of sequences of binary “0” and “1”. 
     Each bitmask corresponds to a string segment in the segmented dictionary  2650  and has a bit in a position corresponding to the end character of the string segment set to “true” (binary 1). The position is relative to the end character of the first simple string of the complex string. 
       FIG. 29  illustrates a state-array  2940  having one Boolean state variable  2950  per complex string in the basic complex dictionary  520 . The Boolean state variables  2950  are individually identified as  2950 - 0  to  2950 - 15 , where the reference numeral  2950 - j  corresponds to a complex string  2510 - j  of the complex dictionary  520 . A position array  2920  has an entry  2930 - j  indicating a last position of the input data at which a simple string belonging to complex string  2510 - j  was detected. The position array  2920  and the state array  2940  are used in the algorithm depicted in  FIGS. 9-11 . 
     The invention thus provides a computationally efficient method for screening a data stream to detect and locate complex strings belonging to a basic complex dictionary. The basic complex dictionary may comprise a very large number of complex strings, each including coherent strings and ambiguous strings. The method is partly based on establishing equality of coherent strings and congruence of ambiguous strings, where congruence of any two characters is based on their joint membership to one of predefined character classes. 
     The method is well adapted to software realization in a single-processor or multi-processor computing environments. The segmentation process of the basic complex dictionary into a segmented dictionary and associated segment descriptor and bitmasks, as illustrated in  FIG. 7 , is performed only when complex strings are added to, or deleted from, the basic complex dictionary. The process may, therefore, be implemented in a computing facility other than the computing facility used for executing the real-time processes of the string-search module  528  of  FIG. 5 , which is further detailed in  FIG. 8 . 
     Furthermore, in a multi-processor environment, the processes implemented by the two basic components  820  and  840  of the string-search module  528 , may be pipelined to increase the rate at which complex strings can be detected and, hence, enable handling data streams of high flow rates. 
     Although specific embodiments of the invention have been described in detail, it should be understood that the described embodiments are intended to be illustrative and not restrictive. Various changes and modifications of the embodiments shown in the drawings and described in the specification may be made within the scope of the following claims without departing from the scope of the invention in its broader aspect.

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