Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Machine Learning for IT Security

  • Philip K. Chan
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_505

Definition

The prevalence of information technology (IT) across all segments of society, greatly improves the accessibility of information, however, it also provides more opportunities for individuals to act with malicious intent. Intrusion detection is the task of identifying attacks against computer systems and networks. Based on data/behavior observed in the past, machine learning methods can automate the process of building detectors for identifying malicious activities.

Motivation and Background

Cyber security often focuses on preventing attacks using authentication, filtering, and encryption techniques, but another important facet is detecting attacks once the preventive measures are breached. Consider a bank vault: thick steel doors prevent intrusions, while motion and heat sensors detect intrusions. Prevention and detection complement each other to provide a more secure environment.

How do we know if an attack has occurred or has been attempted? This requires analyzing huge...

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Recommended Reading

  1. Anderson, D., Lunt, T., Javitz, H., Tamaru, A., & Valdes, A. (1995). Detecting unusual program behavior using the statistical component of the next-generation intrusion detection expert system (NIDES). Technical Report SRI-CSL-95-06, SRI.Google Scholar
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  11. Sekar, R., Bendre, M., Dhurjati, D., & Bollinen, P. (2001). A fast automaton-based method for detecting anomalous program behaviors. In Proceeding of IEEE symposium security and privacy (pp. 144–155). Oakland, CA.Google Scholar
  12. Warrender, C., Forrest, S., & Pearlmutter, B. (1999). Detecting intrusions using system calls: Alternative data models. In IEEE symposium on security and privacy (pp. 133–145). Los Alamitos, CA.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Philip K. Chan

There are no affiliations available