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DESIGN AND EVALUATION OF HIDDEN MARKOV MODEL BASED ARCHITECTURES FOR DETECTION OF INTERLEAVED MULTI-STAGE NETWORK ATTACKS

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<p>Nowadays, the pace of coordinated cyber security crimes has become drastically
more rapid, and network attacks have become more advanced and diversified. The
explosive growth of network security threats poses serious challenges for building
secure Cyber-based Systems (CBS). Existing studies have addressed a breadth of
challenges related to detecting network attacks. However, there is still a lack of
studies on the detection of sophisticated Multi-stage Attacks (MSAs).
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<p>The objective of this dissertation is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved MSAs. We present
the interleaving concept and investigate how interleaving multiple MSAs can deceive
intrusion detection systems. Using one of the important statistical machine learning
(ML) techniques, Hidden Markov Models (HMM), we develop three architectures that
take into account the stealth nature of the interleaving attacks, and that can detect
and track the progress of these attacks. These architectures deploy a set of HMM
templates of known attacks and exhibit varying performance and complexity.
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<p>For performance evaluation, various metrics are proposed which include (1) attack
risk probability, (2) detection error rate, and (3) the number of correctly detected
stages. Extensive simulation experiments are conducted to demonstrate the efficacy
of the proposed architecture in the presence of multiple multi-stage attack scenarios,
and in the presence of false alerts with various rates.
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  1. 10.25394/pgs.9804440.v1
Identiferoai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9804440
Date16 October 2019
CreatorsTawfeeq A Shawly (7370912)
Source SetsPurdue University
Detected LanguageEnglish
TypeText, Thesis
RightsCC BY 4.0
Relationhttps://figshare.com/articles/DESIGN_AND_EVALUATION_OF_HIDDEN_MARKOV_MODEL_BASED_ARCHITECTURES_FOR_DETECTION_OF_INTERLEAVED_MULTI-STAGE_NETWORK_ATTACKS/9804440

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