Savoir-faire malware tracking down in Delay Tolerant Networks
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 3, Issue 5
Abstract
With the universal presence of short-range connectivity technologies (e.g., Bluetooth and, more recently, Wi-Fi Direct) in the consumer electronics market, the delay tolerant-network (DTN) model is becoming a viable alternative to the traditional infrastructural model. The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies. In modern network the malware is one of the serious issues where it can be identified by many roles such as email spam, Denial of service and Trojan like viruses. DTN (Delay Tolerant Network) suffered from the above malware related problems. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on Naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spam’s and detecting bonnets. We identify two unique challenges for extending Bayesian malware detection to DTNs (“insufficient evidence vs. evidence collection risk” and “filtering false evidence sequentially and distributed”), and propose a simple yet effective method, look-ahead, to address the challenges.
Authors and Affiliations
Raksha R S, Rakshitha P G, Shalini M S, Shwetha L, Mrs. Ayesha Taranum
Keywords
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- EP ID EP20373
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