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Home Processing Data Computer-network-intrusion-detection

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Details
Inventors: Diep, Thanh A.;
Assignee: Visa International Service Association (Foster City, CA)
Primary Examiner: Wright; Norman M.
Assistant Examiner:
Attorney, Agent or Firm: Beyer Weaver & Thomas, LLP

Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses statistical analysis to match user commands and program names with a template sequence. Discrete correlation matching and permutation matching are used to match sequences. The result of the match is input to a feature builder and then a modeler to produce a score. The score indicates possible intrusion. A sequence of user commands and program names and a template sequence of known harmful commands and program names from a set of such templates are retrieved. A closeness factor indicative of the similarity between the user command sequence and a template sequence is derived from comparing the two sequences. The user command sequence is compared to each template sequence in the set of templates thereby creating multiple closeness or similarity measurements. These measurements are examined to determine which sequence template is most similar to the user command sequence. A frequency feature associated with the user command sequence and the most similar template sequence is calculated. It is determined whether the user command sequence is a potential intrusion into restricted portions of the computer network by examining output from a modeler using the frequency feature as one input.

DETAILED DESCRIPTION To achieve the foregoing, methods, apparatus, and computer-readable medium are disclosed which provide computer network intrusion detection.
In one aspect of the invention, a method of detecting an intrusion in a computer network is disclosed.
A sequence of user commands and program names and a template sequence of known harmful commands and program names from a set of such templates are retrieved.
A closeness factor indicative of the similarity between the user command sequence and the template sequence is derived from comparing the two sequences.
The user command sequence is compared to each template sequence in the set of templates thereby creating multiple closeness factors.
The closeness factors are examined to determine which sequence template is most similar to the user command sequence.
A frequency feature associated with the user command sequence and the most similar template sequence is calculated.
It is then determined whether the user command sequence is a potential intrusion into restricted portions of the computer network by examining output from a modeler using the frequency feature as one input.
Advantageously, network intrusions can be detected using matching metrics that are efficient and simple to maintain and understand.
In one embodiment, the user command sequence is obtained by chronologically logging commands and program names entered in the computer network thereby creating a command log, and then arranging the command log according to individual users on the computer network.
The user command sequence is identified from the command log using a predetermined time period.
In another embodiment, the frequency of the user command sequence occurring in a command stream created by a network user from a general population of network users is determined.
Another frequency value of how often the most similar sequence template occurs in a command stream created by all network users in the general population of network users is determined.
The two frequency values are used to calculate a frequency feature



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