International Journal of Advanced Networking and Applications

P. Ramesh Kumar ¹, P. Srinivasulu ¹

Affiliations
1 Department of Computer Science and Engineering, V.R.Siddhartha Engineering College, Vijayawada, IN

Abstract

Many emerging Web and Internet applications are based on a group communication model. Securing group communication is an important Internet design issue. A Key Graph approach has been used to implement the group key management and it is used to provide secure group communication. The group key management can be done in two ways: 1. Individual rekeying 2. Batch rekeying. Individual Rekeying is the process of rekeying after each join or leave request. The problem with individual rekeying is inefficiency and out-of-sync problem between keys and data. A batch rekeying using MARKING ALGORITHM can overcome the problems faced in the individual rekeying. The paper applies Batch rekeying by Marking Algorithm on the B-Tree (2-3 trees) and NSBHO (Non Splitting Balancing Higher Order) tree. The Analyzing done on the key server’s processing cost for batch rekeying in B-Tree and NSBHO tree. The proposed NSBHO (Non-Split Balancing High-Order) tree in which balancing tree after member joining does not involve node splitting. The implementation shows that the NSBHO tree has better average-case rekeying performance and far superior worst-case rekeying performance than a B-tree.

Keywords

Balanced Tree, Dynamic Group, Group Key Management, High-Order Tree, Secure Multicast.

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P. Srinivasulu ¹, J. Ranga Rao ¹, I. Ramesh Babu ²

Affiliations
1 Department of Computer Science and Engineering, V R Siddhartha Engineering College, Vijayawada, IN
2 Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, IN

Abstract

In the faceless world of the Internet, online fraud is one of the greatest reasons of loss for web merchants. Advanced solutions are needed to protect e-businesses from the constant problems of fraud. Many popular fraud detection algorithms require supervised training, which needs human intervention to prepare training cases. Since it is quite often for an online transaction database to have Terabyte-level storage, human investigation to identify fraudulent transactions is very costly. This paper describes the automatic design of user profiling method for the purpose of fraud detection. We use a FP (Frequent Pattern) Tree rule-learning algorithm to adaptively profile legitimate customer behavior in a transaction database. Then the incoming transactions are compared against the user profile to uncover the anomalies. The anomaly outputs are used as input to an accumulation system for combining evidence to generate high-confidence fraud alert value. Favorable experimental results are presented.

Keywords

Fraud Detection, FP Tree, Anomalies, Adaptive Mining, Association Mining.

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