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A distributed approach for predicting malicious activities in a network from a streaming data with support vector machine and explicit random feature mapping

Publication Type : Journal Article

Thematic Areas : Scientific reports

Publisher : IIOAB Journal

Authors : Dr. Soman K. P., P. Poornachandran; B. Premjith;

Source : IIOAB Journal, Volume 7, Number 7, p.24-29 (2016)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989170621&partnerID=40&md5=e4d48630c753038be165bc0d4812c6e2

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Computer Science

Year : 2016

Abstract : Technology reduces human effort. However technological advancements always bring threat to personal as well as organizational security, mainly because we all are connected to the internet. Therefore, ensuring cyber security becomes the major topic of discussion. As the magnitude of activities over the internet is unimaginable, envisioning the characteristics of network activities whether it is malicious or good, coming from a stream of data in real time is really a tough task. To tackle this problem, in this paper, we propose a distributive approach based on Support Vector Machine (SVM) with explicit random feature mapping and features mapping is obtained using Compact random feature maps (CRAFTMaps) algorithm. Distributing the job achieves notable improvement in the total prediction time. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.

Cite this Research Publication : P. Poornachandran, B. Premjith, and Dr. Soman K. P., “A distributed approach for predicting malicious activities in a network from a streaming data with support vector machine and explicit random feature mapping”, IIOAB Journal, vol. 7, pp. 24-29, 2016.

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