Cigital, Inc.

Christoph Cornelius Michael

Anup K Ghosh

Michael Schatz

Aaron Schwartzbard

An intrusion detection system (IDS) that uses application monitors for detecting application-based attacks against computer systems. The IDS implements application monitors in the form of a software program to learn and monitor the behavior of system programs in order to detect attacks against computer hosts. The application monitors implement machine learning algorithms to provide a mechanism for learning from previously observed behavior in order to recognize future attacks that it has not seen before. The application monitors include temporal locality algorithms to increased the accuracy of the IDS. The IDS of the present invention may comprise a string-matching program, a neural network, or a time series prediction algorithm for learning normal application behavior and for detecting anomalies.

Computer intrusion detection system and method based on application monitoring

A system and method by which novel, malicious execution traces may be detected by applying a combination of finite automation and heuristic analysis techniques. Such execution traces may be obtained by instrumenting system-level operating system calls, as well as by other techniques, such as, but not limited to, reading error log files, such as Windows NT event logs. With proper instrumentation, known good and known malicious programs may be run and their execution traces monitored. From such monitoring, a model may be derived, which can indicate those execution traces typically associated with malicious software. With this information, novel malicious programs which invoke execution traces similar to known malicious traces may be detected, and such programs may be stopped before significant damage can occur.

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Reston, VA, United States of America

Christoph Cornelius Michael