Informatics Publishing Limited

S. Umamaheswari ¹, Arun M. Kudtarkar ², T. Hamsapriya ³, R. Mekala ⁴

Affiliations
1 Department of Computer Science in The Oxford College of Science, Bangalore, Karnataka, IN
2 Oxford College of Science at Department of Computer Science, Bangalore, Karnataka, IN
3 Department of Information Technology in PSG College of Technology, Coimbatore, TamilNadu, IN
4 Sasurie Academy of Engineering, Coimbatore, IN

Abstract

The openness of P2P network gives a perfect situation for attackers to spread malicious content in the network. In this paper we investigate the design of a secured Information-Sharing Incentive Scheme (ISIS) for decentralized peer-to-peer network (P2P) to ensure trustworthy file sharing. The dynamically growing ISIS increases the efficiency of a P2P network by means of minimal malicious peers and malicious files by establishing trust among good peers as well as identifying and isolating the malicious peers. The ISIS is robust, predictive and invariant under varying network conditions and peer resource constraints.

Keywords

Free Riders, Malicious Peers, P2P Network, Reputation System.

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T. Hamsapriya ¹, D. Karthika Renuka ¹, M. Raja Chakkaravarthi ¹

Affiliations
1 Department of Information Technology, PSG College of Technology, Tamil Nadu, IN

Abstract

E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

Keywords

Machine Learning, J48, MLP, Naive Bayesian, Spam Classification, FBL, Feature Subset Selection.

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