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Omar Alkhateeb, "Adaptive Anomaly Detection: Batch Learning," Master's Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), September 2021. (Advisor: Hossein Doroud; Referees: Falko Dressler and Thomas Sikora)


Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g., data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats. A firewall is well-known as the first layer of defense for a computer network. However, intruders developed new techniques to bypass firewalls and access computer networks during the last decades. As a result, researchers introduced an Intrusion Detection System (IDS) as an additional layer of defense to make the life of intruders more difficult. Intrusion Detection Systems can detect attacks using defined patterns in the traffic (Signature-based Intrusion Detection System) or deviations of the regular network traffic (Anomaly-based Intrusion Detection System). However, these approaches face some drawbacks; a change in the attack patterns makes Signature-based IDS blind in detecting attacks; the dynamic nature of the network traffic makes it difficult to define the normal profile of the network for Anomaly-based IDS. Therefore, I developed in this thesis an anomaly-based Lifelong Learning Intrusion Detection System (LL-IDS) with the help of Snort, which is the most well-known IDS. This anomaly detection system uses a lifelong machine learning algorithm to learn the normal traffic and a batch to retrain from its false positives. Three lifelong machine learning algorithms were chosen to be implemented separately in the IDS and compared on the UNSW-NB15 dataset using different metrics. The algorithm with the highest detection rate and the lowest classification time consumption was implemented with a subset of the feature set (selected by a feature selection algorithm) to compare it with Snort standalone. LL-IDS showed a better detection rate (61.99% precision and 83.40% recall) than Snort standalone (61.54% precision and 51.91% recall).

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Omar Alkhateeb

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    author = {Alkhateeb, Omar},
    title = {{Adaptive Anomaly Detection: Batch Learning}},
    advisor = {Doroud, Hossein},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {9},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Master's Thesis},
    year = {2021},

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