Penalty-Reward Based Instance Selection Method in Cloud Environment Using the Concept of Nearest Neighbor

Abstract Cloud computing is the distribution of computing resources over the Internet. A shared pool of resources, including data storage space, computer processing power and applications are provided by Cloud computing. In spite of being attractive, it challenges with new security threats when it comes to deploying an Intrusion Detection System (IDS) in Cloud environment. It requires a lot of time to process the Cloud dataset and produce proper classification strategy. A Penalty-Reward based instance selection method to reduce the Cloud dataset is proposed here. Using this method all the noisy and boundary instances are removed from the training dataset. After that Reverse Nearest Neighbor Reduction (RNNR) method is applied on the remaining instances to select all relevant instances from them. This helps to reduce the training time as well as to produce better classification accuracy for IDS.

[1]  Nicolás García-Pedrajas,et al.  Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts , 2010, Artif. Intell..

[2]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[3]  Nitasha Hasteer,et al.  Intrusion Detection and Prevention in Cloud Environment: A Systematic Review , 2013 .

[4]  Vern Paxson,et al.  Bro: a system for detecting network intruders in real-time , 1998, Comput. Networks.

[5]  Partha Ghosh,et al.  An Efficient Hybrid Multilevel Intrusion Detection System in Cloud Environment , 2014 .

[6]  Pritika Mehra,et al.  A brief study and comparison of Snort and Bro Open Source Network Intrusion Detection Systems , 2012 .

[7]  Huan Liu,et al.  On Issues of Instance Selection , 2002, Data Mining and Knowledge Discovery.

[8]  T. Basar,et al.  Intrusion Response as a Resource Allocation Problem , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[9]  Padhraic Smyth,et al.  Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[10]  G. Gates,et al.  The reduced nearest neighbor rule (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[11]  Swarnpreet Singh,et al.  Cost breakdown of Public Cloud Computing and Private Cloud Computing and Security Issues , 2012 .

[12]  Feng Pan,et al.  Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications , 2007, IEEE Transactions on Parallel and Distributed Systems.

[13]  Hugh B. Woodruff,et al.  An algorithm for a selective nearest neighbor decision rule (Corresp.) , 1975, IEEE Trans. Inf. Theory.

[14]  Partha Ghosh,et al.  An Efficient Cloud Network Intrusion Detection System , 2015 .

[15]  Hui Xiong,et al.  Enhancing data analysis with noise removal , 2006, IEEE Transactions on Knowledge and Data Engineering.

[16]  Marcin Blachnik,et al.  Ensembles of Instance Selection Methods based on Feature Subset , 2014, KES.

[17]  Roberto Di Pietro,et al.  Secure virtualization for cloud computing , 2011, J. Netw. Comput. Appl..

[18]  Trent Jaeger,et al.  Process firewalls: protecting processes during resource access , 2013, EuroSys '13.

[19]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[20]  Rajkumar Buyya,et al.  Utility Computing on Global Grids , 2012 .

[21]  G. Gates.  The Reduced Nearest Neighbor Rule , 1998 .

[22]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[23]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[24]  Ahmed Shawish,et al.  Cloud Computing: Paradigms and Technologies , 2014 .