Inline detection of Denial of Service Attacks in Software Defined Networking using the Hotelling Chart
Author(s) -
Faycal Bensalah,
Najib El Kamoun,
Mohammed-Alamine El Houssaini
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.11.010
Subject(s) - denial of service attack , computer science , software defined networking , application layer ddos attack , computer security , flooding (psychology) , trinoo , scalability , computer network , single point of failure , network security , network architecture , software , distributed computing , the internet , operating system , psychology , psychotherapist
Software Defined Network (SDN) has become the true trend of the IT service model offering a cost-effective and scalable processing solution. Although central control is the major asset of the SDN, it is also a single point of failure if it is made inaccessible by a distributed denial of service (DDoS) attack. Despite the number of existing traditional detection solutions, DDoS attacks continue to increase in frequency, volume and severity. This paper provides an analysis of the problem by examining the impact on security, particularly for distributed denial of service (DDoS) attacks that is still a threat to SDN software-defined networks and suggests the proposal and implementation of a DDoS attack detection algorithm. Based on an architecture that integrates highly programmable network monitoring and a flexible control structure to enable fast and specific attack detection. To cope with the new architecture, we propose an attack detection system based on a statistical model that can address the problem of flooding attack and bandwidth attack. The simulation results show that our architecture can effectively address the security challenges posed by the new network paradigm and our attack detection system can effectively signal various attacks using real-world network traffic.
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