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Felix Erlacher, "Efficient Intrusion Detection in High-Speed Networks," PhD Thesis, Department of Computer Science, Paderborn University (UPB), June 2019. (Advisor: Falko Dressler; Referee: Felix C. Freiling)


To keep today's computer networks up and running, it is paramount to detect all attacks and malicious activities contained in the network traffic. This makes network intrusion detection an integral part of every IT security strategy. In this PhD thesis we study the problem of intrusion detection in high-speed networks. To achieve sufficient accuracy, state-of-the-art Network Intrusion Detection Systems (NIDS) apply performance intensive procedures like Deep Packet Inspection (DPI)-methods on network packets and, thus, can not cope with the traffic rates in high-throughput networks. The fact that high-throughput connections are nowadays widespread even in smaller corporate or campus networks, stresses for more efficient detection approaches. This thesis proposes novel methods for efficient intrusion detection in such scenarios. In order to get an understanding of today’s threat landscape, we give an overview of the attacks which arose with the introduction of the so called Web 2.0. We analyze current mitigation techniques and point out open research problems. Further, we present an approach which increases the efficiency of anomaly-based NIDS by combining multiple anomaly detection algorithms on a single computer. Our novel load allocation scheme mitigates random packet drops caused by the high performance-demand of the combined algorithms. To increase the network throughput performance of network monitoring appliances in general and NIDS in particular, we propose two methods for preprocessing HTTP traffic before analysis. We show that both approaches significantly reduce the data portion to be analyzed while retaining the relevant parts for intrusion detection. Then we present our novel signature-based NIDS called FIXIDS, which takes as input HTTP-enriched IPFIX Flows. By applying HTTP-related signatures from the widely used NIDS Snort, it guarantees that thousands of up-to-date and community validated attack descriptions are available. Results show that FIXIDS is able to analyze the HTTP-portion of typical internet traffic even at rates of more than 9.5 Gbit/s. In the final contribution we propose a malicious HTTP traffic generator for NIDS evaluation called GENESIDS. It uses Snort signatures as attack descriptions. The evaluation shows that GENESIDS reliably generates a variety of more than 8000 different attacks. Summarizing, we strongly believe that the above contributions significantly increase the efficiency of NIDS in modern high-speed networks.

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Felix Erlacher

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    author = {Erlacher, Felix},
    doi = {10.17619/UNIPB/1-742},
    title = {{Efficient Intrusion Detection in High-Speed Networks}},
    advisor = {Dressler, Falko},
    institution = {Department of Computer Science},
    location = {Paderborn, Germany},
    month = {6},
    referee = {Freiling, Felix C.},
    school = {Paderborn University (UPB)},
    type = {PhD Thesis},
    year = {2019},

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