Improving the Efficiency and Robustness of Intrusion Detection Systems

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Fogla, Prahlad
Lee, Wenke
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With the increase in the complexity of computer systems, existing security measures are not enough to prevent attacks. Intrusion detection systems have become an integral part of computer security to detect attempted intrusions. Intrusion detection systems need to be fast in order to detect intrusions in real time. Furthermore, intrusion detection systems need to be robust against the attacks which are disguised to evade them. We improve the runtime complexity and space requirements of a host-based anomaly detection system that uses q-gram matching. q-gram matching is often used for approximate substring matching problems in a wide range of application areas, including intrusion detection. During the text pre-processing phase, we store all the q-grams present in the text in a tree. We use a tree redundancy pruning algorithm to reduce the size of the tree without losing any information. We also use suffix links for fast linear-time q-gram search during query matching. We compare our work with the Rabin-Karp based hash-table technique, commonly used for multiple q-gram matching. To analyze the robustness of network anomaly detection systems, we develop a new class of polymorphic attacks called polymorphic blending attacks, that can effectively evade payload-based network anomaly IDSs by carefully matching the statistics of the mutated attack instances to the normal profile. Using PAYL anomaly detection system for our case study, we show that these attacks are practically feasible. We develop a formal framework which is used to analyze polymorphic blending attacks for several network anomaly detection systems. We show that generating an optimal polymorphic blending attack is NP-hard for these anomaly detection systems. However, we can generate polymorphic blending attacks using the proposed approximation algorithms. The framework can also be used to improve the robustness of an intrusion detector. We suggest some possible countermeasures one can take to improve the robustness of an intrusion detection system against polymorphic blending attacks.
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