AbstractsComputer Science

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.