Detection of Denial of QoS Attacks on DiffServ Networks.

security;deception;QoS;compromise;statistical intrusion detection;hack;anomaly detection

Mahadik, Vinay A. ; Dr. Douglas S. Reeves, Committee Chair,Dr. Peng Ning, Committee Member,Dr. Jon Doyle, Committee Member,Dr. Gregory Byrd, Committee Member,Mahadik, Vinay A. ; Dr. Douglas S. Reeves ; Committee Chair ; Dr. Peng Ning ; Committee Member ; Dr. Jon Doyle ; Committee Member ; Dr. Gregory Byrd ; Committee Member

University:North Carolina State University

关键词: security;    deception;    QoS;    compromise;    statistical intrusion detection;    hack;    anomaly detection;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/1743/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

In this work, we describe a method of detecting denial of Quality of Service (QoS) attacks on Differentiated Services (DiffServ) networks. Our approach focusses on real time and quick detection, scalability to large networks, and a negligible false alarm generation rate. This is the first comprehensive study on DiffServ monitoring. Our contributions to this research area are 1. We identify several potential attacks, develop/use research implementations of each on our testbed and investigate their effects on the QoS sensitive network flows. 2. We study the effectiveness of several anomaly detection approaches; select and adapt SRI's NIDES statistical inference algorithm and EWMA Statistical Process Control technique for use in our anomaly detection engine. 3. We then emulate a Wide Area Network on our testbed. We measure the effectiveness of our anomaly detection system in detecting the attacks and present the results obtained as a justification of our work. 4. We verify our findings through simulation of the network and the attacks on NS2 (the Network Simulator, version 2). We believe that given the results of the tests with our implementation of the attacks and the detection system, further validated by the simulations, the method is a strong candidate for QoS-intrusion detection for a low-cost commercial deployment.