A Neural Network Based Distributed Intrusion Detection System on Cloud Platform

Intrusion detection system (IDS) is an important component to maintain network security. Also, as the cloud platform is quickly evolving and becoming more popular in our everyday life, it is useful and necessary to build an effective IDS for the cloud. However, existing intrusion detection techniques will be likely to face challenges when deployed on the cloud platform. The pre-determined IDS architecture may lead to overloading of a part of the cloud due to the extra detection overhead. This thesis proposes a neural network based IDS, which is a distributed system with an adaptive architecture, so as to make full use of the available resources without overloading any single machine in the cloud. Moreover, with the machine learning ability from the neural network, the proposed IDS can detect new types of attacks with fairly accurate results. Evaluation of the proposed IDS with the KDD dataset on a physical cloud testbed shows that it is a promising approach to detecting attacks in the cloud infrastructure.

Name thesis:degree_name

Master of Science in Engineering

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Engineering (Computer Science)

Grantor dc:publisher

University of Toledo

Year dc:date

2013

Author dc:creator

• Li, Zhe

Contributors dc:contributor

• Wang, Lingfeng
• Sun, Weiqing

• Computer Engineering
• Computer Science
• Distributed IDS
• Neural network
• Cloud security
• Anomaly detection

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=toledo1364835027

OAI identifier oai:identifier

oai:etd.ohiolink.edu:toledo1364835027