Approximate string matching

This section of our chapter excerpt on network algorithms suggests that approximate string matching could be implemented at wire speeds by using minwise hashing and random projection theories.


Solution provider takeaway: Approximately detecting strings in payloads serves as an even more challenging issue for clients than searching for multiple strings. This section of our chapter excerpt from the book "Network Security: Know It All" describes the process of minwise hashing and random projections.

Download the .pdf of the chapter here.

This section briefly considers an even harder problem, that of approximately detecting strings in payloads. Thus instead of settling for an exact match or a prefix match, the specification now allows a few errors in the match. For example, with one insertion "perl.exe" should match "perl.exe" where the intruder may have added a character.

While the security implications of using the mechanisms described next need much more thought, the mechanisms themselves are powerful and should be part of the arsenal of designers of detection mechanisms.

The first simple idea can handle substitution errors. A substitution error is a replacement of one or more characters with others. For example, "parl.exe" can be obtained from "perl.exe" by substituting "a" for "e." One way to handle this is to search not for the complete string but for one or more random projections of the original string.

Checking for matching with a random projection of the target string "babar" allows the detecting of similar strings with substitution errors in the payload.

For example, in Figure 4.4, instead of searching for "babar" one could search for the first, third, and fourth characters in "babar." Thus the misspelled string "babad" will still be found. Of course, this particular projection will not find a misspelled string such as "rabad." To make it hard for an adversary, the scheme in general can use a small set of such random projections. This simple idea is generalized greatly in a set of papers on locality-preserving hashing.

Interestingly, the use of random projections may make it hard to efficiently shift one character to the right. One alternative is to replace the random projections by deterministic projections. For example, if one replaces every string by its two halves and places each half in an Aho-Corasick trie, then any one substitution error will be caught without slowing down the Aho-Corasick processing. However, the final efficiency will depend on the number of false alarms.

The simplest random projection idea, described earlier, does not work with insertions or deletions that can displace every character one or more steps to the left or right. One simple and powerful way of detecting whether two or more sets of characters, say, "abcef" and "abfecd," are similar is by computing their resemblance.

The resemblance of two sets of characters is the ratio of the size of their intersection to the size of their union. Intuitively, the higher the resemblance, the higher the similarity. By this definition, the resemblance of "abcef" and "abfecd" is 5/6 because they have five characters in common.

Unfortunately, resemblance per se does not take into account order, so "abcef" completely resembles "fecab." One way to fix this is to rewrite the sets with order numbers attached so that "abcef" becomes "1a2b3c4e5f" while "fecab" now becomes "1f2e3c4a5b." The resemblance, using pairs of characters as set elements instead of characters, is now nil. Another method that captures order in a more relaxed manner is to use shingles by forming the two sets to be compared using as elements all possible substrings of size k of the two sets.

Resemblance is a nice idea, but it also needs a fast implementation. A naive implementation requires sorting both sets, which is expensive and takes large storage. Broder's idea is to quickly compare the two sets by computing a random (P3a, trade certainty for time) permutation on two sets. For example, the most practical permutation function on integers of size at most m - 1 is to compute P(X) = ax +b mod m, for random values of a and b and prime values of the modulus m.

For example, consider the two sets of integers {1, 3, 5} and {1, 7, 3}. Using the random permutation {3x + 5 mod 11}, the two sets become permuted to {8, 3, 9} and {8, 4, 3}. Notice that the minimum values of the two randomly permuted sets (i.e., 3) are the same.

Intuitively, it is easy to see that the higher the resemblance of the two sets, the higher the chance that a random permutation of the two sets will have the same minimum. Formally, this is because the two permuted sets will have the same minimum if and only if they contain the same element that gets mapped to the minimum in the permuted set. Since an ideal random permutation makes it equally likely for any element to be the minimum after permutation, the more elements the two sets have in common, the higher the probability that the two minimums match.

More precisely, the probability that two minimums match is equal to the resemblance. Thus one way to compute the resemblance of two sets is to use some number of random permutations (say, 16) and compute all 16 random permutations of the two sets. The fraction of these 16 permutations in which the two minimums match is a good estimate of the resemblance.

This idea was used by Broder to detect the similarity of Web documents. However, it is also quite feasible to implement at high link speeds. The chip must maintain, say, 16 registers to keep the current minimum using each of the 16 random hash functions. When a new character is read, the logic permutes the new character according to each of the 16 functions in parallel. Each of the 16 hash results is compared in parallel with the corresponding register, and the register value is replaced if the new value is smaller.

At the end, the 16 computed minima are compared in parallel against the 16 minima for the target set to compute a bitmap, where a bit is set for positions in which there is equality. Finally, the number of set bits is counted and divided by the size of the bitmap by shifting left by 4 bits. If the resemblance is over some specified threshold, some further processing is done.

Once again, the moral of this section is not that computing the resemblance is the solution to all problems (or in fact to any specific problem at this moment) but that fairly complex functions can be computed in hardware using multiple hash functions, randomization, and parallelism. Such solutions interplay principle P5 (use parallel memories) and principle P3a (use randomization).

Network Security Algorithms
  4.1 Searching for multiple strings in packet payloads
  4.2 Approximate string matching
  4.3 IP traceback via probabilistic marking
  4.4 IP traceback via logging
  4.5 Detecting worms

About the book
The chapters contain compiled knowledge from recognized industry experts on topics such as the theory and practice of network security technology, analysis and problem-solving techniques and core security concepts.

Printed with permission from Morgan Kaufmann, a division of Elsevier. Copyright 2008. Network Security: Know It All by James Joshi. For more information about this title and other similar books, please visit

This was last published in November 2008

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