sesa 18(17): e4

Research Article

Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach

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  • @ARTICLE{10.4108/eai.10-1-2019.156245,
        author={Ivan Homoliak and Martin Tekn\o{}s and Mart\^{\i}n Ochoa and Dominik Breitenbacher and Saeid Hosseini and Petr Hanacek},
        title={Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={5},
        number={17},
        publisher={EAI},
        journal_a={SESA},
        year={2018},
        month={12},
        keywords={Classification-Based Intrusion Detection, Adversarial Classification, Non-Payload-Based Obfuscation, Evasion, NetEm, Network Normalizer},
        doi={10.4108/eai.10-1-2019.156245}
    }
    
  • Ivan Homoliak
    Martin Teknös
    Martín Ochoa
    Dominik Breitenbacher
    Saeid Hosseini
    Petr Hanacek
    Year: 2018
    Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach
    SESA
    EAI
    DOI: 10.4108/eai.10-1-2019.156245
Ivan Homoliak1,2,*, Martin Teknös1, Martín Ochoa3,4, Dominik Breitenbacher1, Saeid Hosseini2, Petr Hanacek1
  • 1: Faculty of Information Technology, Brno University of Technology, Bozetechova 1/2, 612 66 Brno, Czech Republic
  • 2: STE-SUTD Cyber Security Laboratory, 8 Somapah Road, 487372, Singapore
  • 3: Department of Applied Mathematics and Computer Science, Universidad del Rosario, Bogotá, Colombia
  • 4: Cyxtera Technologies
*Contact email: ihomoliak@fit.vutbr.cz

Abstract

Machine-learning based intrusion detection classifiers are able to detect unknown attacks, but at the same time they may be susceptible to evasion by obfuscation techniques. An adversary intruder which possesses a crucial knowledge about a protection system can easily bypass the detection module. The main objective of our work is to improve the performance capabilities of intrusion detection classifiers against such adversaries. To this end, we firstly propose several obfuscation techniques of remote attacks that are based on the modification of various properties of network connections; then we conduct a set of comprehensive experiments to evaluate the effectiveness of intrusion detection classifiers against obfuscated attacks. We instantiate our approach by means of a tool, based on NetEm and Metasploit, which implements our obfuscation operators on any TCP communication. This allows us to generate modified network traÿc for machine learning experiments employing features for assessing network statistics and behavior of TCP connections. We perform evaluation on five classifiers: Gaussian Naïve Bayes, Gaussian Naïve Bayes with kernel density estimation, Logistic Regression, Decision Tree, and Support Vector Machines. Our experiments confirm the assumption that it is possible to evade the intrusion detection capability of all classifiers trained without prior knowledge about obfuscated attacks, causing an exacerbation of the TPR ranging from 7.8% to 66.8%. Further, when widening the training knowledge of the classifiers by a subset of obfuscated attacks, we achieve a significant improvement of the TPR by 4.21% – 73.3%, while the FPR is deteriorated only slightly (0.1% – 1.48%). Finally, we test the capability of an obfuscations-aware classifier to detect unknown obfuscated attacks, where we achieve over 90% detection rate on average for most of the obfuscations.