Malware Detection and Elimination using Bayesian Technique and Nymble Algorithm

Background/Objectives: DTN becomes popular because of its ability to cope with the problems in traditional infrastructural model. Like other kinds of network it is also subjected to malware attacks. Methods/Statistical Analysis: Pattern matching technique is so far used. But that is not secure in DTN as there is changing network topology. In this paper, a novel malware processing technique is proposed which uses Bayesian technique and Nymble algorithm. Bayesian technique is used to fabricate a secure DTN and Nymble algorithm helps in removing malware. Findings: Bayesian technique is used in non DTN techniques for malware processing. While using Bayesian techniques to generate a secure DTN without false positive, active attacks, passive attacks, false negative, effect of liars, effect of malware affected nodes, inadequate evidence and malware spreading. All the challenges are addressed using dogmatic filtering, adaptive look ahead, cut off strategy techniques. Nymble algorithm enhances the security of DTN. It provides ambiguity, rate limiting, subjective blacklisting and non-frame ability. Application/Improvements: The proposed techniques are used to identify any abnormal behavior of the nodes and complaints will be posted. Only authenticated users can post complaints. In addition to this, it provides cryptographic security to the users. The non-legitimate nodes in the network will be blocked and displayed. This improves the QoS of the DTN.