Computational Intelligence, Cyber Security and Computational by Muthukrishnan Senthilkumar, Vijayalakshmi Ramasamy, Shina

By Muthukrishnan Senthilkumar, Vijayalakshmi Ramasamy, Shina Sheen, C. Veeramani, Anthony Bonato, Lynn Batten

This booklet goals at selling top of the range learn through researchers and practitioners from academia and on the International convention on Computational Intelligence, Cyber defense, and Computational versions ICC3 2015 prepared by means of PSG collage of know-how, Coimbatore, India in the course of December 17 – 19, 2015. This ebook enriches with ideas in wide parts of analysis like computational modeling, computational intelligence and cyber defense. those rising inter disciplinary study components have helped to unravel multifaceted difficulties and received lot of realization lately. This encompasses conception and purposes, to supply layout, research and modeling of the aforementioned key areas.

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In: Intelligence and Security Informatics Conference (EISIC), pp. 141–147. : Permission-based android malware detection. Int. J. Sci. Technol. Res. : Information gain and a general measure of correlation. , Turner, H. : Applying machine learning classifiers to dynamic Android malware detection at scale. In: Wireless Communications and Mobile Computing, pp. , Batten, L. : Analysis of malicious and benign Android applications. In: 32nd International Conference on Distributed Computing Systems, pp.

J. Sci. Technol. Res. : Information gain and a general measure of correlation. , Turner, H. : Applying machine learning classifiers to dynamic Android malware detection at scale. In: Wireless Communications and Mobile Computing, pp. , Batten, L. : Analysis of malicious and benign Android applications. In: 32nd International Conference on Distributed Computing Systems, pp. 608–616. : Performance evaluation of multi-tier ensemble classifiers for phishing websites. In: 3rd Applications and Technologies in Information Security, pp.

RobotDroid: A lightweight malware detection framework on smartphones. J. : A machine learning approach to android malware detection. In: Intelligence and Security Informatics Conference (EISIC), pp. 141–147. : Permission-based android malware detection. Int. J. Sci. Technol. Res. : Information gain and a general measure of correlation. , Turner, H. : Applying machine learning classifiers to dynamic Android malware detection at scale. In: Wireless Communications and Mobile Computing, pp. , Batten, L.

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