Detecting Zero-Day Attacks Using Contextual Relations

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 185)

Abstract

The focus of this research is a knowledge-based intrusion detection technique that utilizes contextual relations between known attacks to identify zero-day attacks, which are exploits of unknown software vulnerabilities. The proposed technique uses information entropy and linear data transformation to generate feature-based and linear function-based attack profiles. It systematically creates contextual relationships between known attacks to generate attack profiles that capture most likely combinations of activities an attacker might exploit to initiate zero-day attacks. We utilize the similarity among the features of the incoming network connections and these profiles to discover zero-day attacks. Our experiments on benchmark intrusion detection datasets indicate that utilizing contextual relationships to generate attack profiles leads to a satisfactory detection rate of zero-day attacks from network data at different levels of granularity.

Keywords

Intrusion detection Zero-day attacks Contextual relations Entropy IP flows 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information SystemsUniversity of Maryland, Baltimore County (UMBC)BaltimoreUSA

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