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 paper 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.
Distributed IDS Neural network Cloud security Anomaly detection
Zhe Li Weiqing Sun Lingfeng Wang
Department of Electrical Engineering and Computer Science,University of Toledo,2801 W.Bancroft St.,T Department of Engineering Technology,University of Toledo,2801 W.Bancroft St.,Toledo,OH 43606,USA
国际会议
杭州
英文
104-108
2012-10-30（万方平台首次上网日期，不代表论文的发表时间）