Literature Database Entry

rietz2017optimization


Rene Rietz, "Optimization of Network Intrusion Detection Processes," PhD Thesis, Institute of Computer Science and Information and Media Technology, Brandenburg University of Technology, November 2017. (Advisor: Hartmut König; Referees: Falko Dressler and Felix C. Freiling)

Abstract

Intrusion detection is a concept from the field of IT security. Network intrusion detection systems (NIDS) are used in addition to preventative measures, such as firewalls,to enable an automated detection of attacks. Network security threats often consistof multiple attack phases directed against various components of the network. Duringeach attack phase, varying types of security-related events can be observed at variouspoints in the network. Security monitoring, however, is nowadays essentially limitedto the uplink to the internet. Sometimes it is also used to a limited extent at keypoints within a network, but the analysis methods do not have the same depth as atthe uplink. Other areas, such as virtual networks in virtual machines (VMs), are notcovered at all, yet.The aim of this thesis is to improve the detection capability of attacks in local areanetworks. With a glance at the area of safety engineering, it appears efficient to secure these networks thoroughly and to develop additional monitoring solutions onlyfor the remaining problem cases. This entails several challenges for the analysis ofdifferent parts of the TCP/IP stack. The lowermost part of the network stack hasto be analyzed for attacks on network components, such as switches and VM bridges.For this, there is still no technology. Although attacks on the layers 2 and 3, suchas ARP spoofing and rogue DHCP servers in physical networks, can be controlledto some extent by appropriate switches, equivalent methods are not used in virtualnetworks. Therefore, a software-defined networking based approach is proposed tocounteract the respective attacks, which works for physical and virtual networks. Theupper layers are already largely covered by traditional NIDS methods, but the rapidlyincreasing data rates of local area networks often lead to an uncontrolled discardingof traffic due to overload situations in the monitoring stations. Therefore, the drawbacks of current optimization approaches are outlined based on a detailed performanceprofiling of typical intrusion detection systems. A new approach for parallelizing theintrusion detection analysis that copes with the increasing network dynamics is introduced and evaluated. Since further special issues for NIDS particularly go backto the massive use of web technologies in today’s networks, a firewall architecture ispresented which applies novel NIDS methods based on machine learning to identifyweb applications and to ward off malicious inputs. The architecture addresses theentire process chain starting from the data transfer with HTTP via the analysis ofmanipulated web documents to the extraction and analysis of active contents.

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@phdthesis{rietz2017optimization,
    advisor = {K{\"{o}}nig, Hartmut},
    author = {Rietz, Rene},
    institution = {Institute of Computer Science and Information and Media Technology},
    location = {Cottbus, Germany},
    month = {11},
    referee = {Dressler, Falko and Freiling, Felix C.},
    school = {Brandenburg University of Technology},
    title = {{Optimization of Network Intrusion Detection Processes}},
    type = {PhD Thesis},
    year = {2017},
   }
   
   

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