Network intrusion detection and countermeasure selection in virtual network (NIDCS)
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Date
October 2015
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Abstract
Intrusion in a network or a system is a problem today as the trend of successful
network attacks continue to rise. Intruders can explore vulnerabilities of a network
system to gain access in order to deploy some virus or malware such as Denial of
Service (DOS) attack. In this work, a frequency-based Intrusion Detection System
(IDS) is proposed to detect DOS attack. The frequency data is extracted from the
time-series data created by the traffic flow using Discrete Fourier Transform (DFT).
An algorithm is developed for anomaly-based intrusion detection without any false
alarm which further detect known and unknown attack signature in a network. The
frequency of the traffic data of the virus or malware would be inconsistent with the
frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion
Detection System called CTA-IDS is introduced to further detect inside attackers in a
network. The strategy is effective in detecting abnormal content in the traffic data
during information passing from one node to another and also detects known attack
signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2)
software.
Description
A thesis submitted to the Department of Electrical and Electronic Engineering, College of Engineering
In partial fulfillment of the requirements for the degree of Master of Science (Telecommunication Engineering), 2015