Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach
| Autor: | Ivan Homoliak, Saeid Hosseini, Martín Ochoa, Petr Hanacek, Dominik Breitenbacher, Martin Teknos |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Cryptography and Security Computer science business.industry Payload (computing) Decision tree Evasion (network security) 020206 networking & telecommunications 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Support vector machine Naive Bayes classifier C.2.0 Obfuscation Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Cryptography and Security (cs.CR) |
| DOI: | 10.48550/arxiv.1805.02684 |
| Popis: | 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 traffic for machine learning experiments employing features for assessing network statistics and behavior of TCP connections. We perform the evaluation of five classifiers: Gaussian Naive Bayes, Gaussian Naive 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. |
| Databáze: | OpenAIRE |
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