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Simulations monitoring queue sizes confirmed that some algorithms which are stable in homogeneous setting, are not stable under this setting.
It is hoped that this study with inform and enlighten cloud service providers about new ways to improve the security of the cloud in the presence of failure/attacks.
Finite state machines (FSM) of the model were produced and verified then analyzed using probabilistic model checker.
Results indicate that given certain conditions the proposed model will be in a state that efficiently utilize resources despite the presence of attack. Genetic Algorithms in Search, Optimization and Machine Learning. Protocol Anomaly Detection for Network-based Intrusion Detection, SANS Institute, GSEC Practical Assignment Version 1.2f, 2001 M. 263-268." Sampada Chavan, Khusbu Shah, Neha Dave and Sanghamitra Mukherjee” Adaptive Neuro-Fuzzy Intrusion Detection Systems” Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04) IEEE 2004. Detecting denial-of-service attacks with incomplete audit data. of the 14th Int'nl Conference on Computer Communications and Networks (ICCCN 2005) (October 2005), IEEE Computer Society, pp. 95-022, COAST Laboratory, Department of Computer Sciences, Purdue University, March 1994. Mark Crosbie, Gene Spafford, Defending a Computer System using Autonomous Agents, Technical report No. In Advanced Computing and Intelligent Technologies (ICACIE), 2016 First International Conference on. A Novel algorithm for Network Anomaly Detection using Adaptive Machine Learning. (2010) Data Clustering Using K-Mean Algorithm For Network Intrusion Detection, Thesis, Lovely Professional University, Jalandhar. (2012) ‘Survey on data mining techniques to enhance intrusion detection’, International Conference on Computer Communication and Informatics, ICCI-2012, Coimbatore, India. Intrusion Detection and Correlation: Challenges and Solutions.