Advanced Multimedia Processing Lab -- Projects -- Pattern Recognition for Intrusion Detection

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Project - Pattern Recognition Tools for Intrusion Detection

 

 

Contents

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Team Member

 

Devi Parikh

dparikh@andrew.cmu.edu

 

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Motivation and Goal

 

Conventional intrusion detection methods in the field of computer security are anomaly detection and misuse detection the former suffers from high false alarm rates while the latter lacks generalization capabilities and cannot detect new attack types. Pattern recognition techniques have been found to strike a fine balance in this trade off.

 

The goal of this work is thus to develop an effective classification algorithm for intrusion detection, that utilizes pattern recognition techniques to incorporate the following capabilities in the classification system:

Thorough statistical analysis of the data used is to be performed for feature selection purposes and to ensure reliable results.

 

Ongoing work involves integrating these different aspects to develop a complete algorithm for adaptively evolving intrusion detection that exploits the ensemble of classifiers approach to achieve effective intrusion detection that combines information from multiple sources and is tuned towards minimizing the cost of the errors.

 

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Approach and Results

 

We work with the DARPA/MIT KDD database. Details about the approaches used and results obtained for these different tasks can be found in the related publications below.

 

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Publications

The following are other documents that contain further details about certain aspects of the project:

The work is being sponsored by Institute for Information Industry (III), Taiwan.

 

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Contact

 

Any suggestions or comments are welcome. Please send them to Devi Parikh 

 

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