Design and Development of Humiliating of C-Worm Utilizing an Arbitrary Visualizing


  • S. Ravichandran Research Scholar in Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India
  • M. Umamaheswari Professor in Department of Information Technology, RRASE College of Engineering, Chennai, Tamil Nadu, India



Intrusion Detection, Denial of Service, Pattern, ANN, Malicious, Network


A worm is a malicious self-replicating programs, it is intended to feast via computer links. The computer worms are one method of malware beside with Trojans and Viruses. The active worms impersonate main refuge threats to that an Internet. This is the ability of active worms to continuously propagate in the computers on the Internet as an automated fashion. The Active worms changes through their circulation, and thus, pose large tasks to preserve alongside them. This manuscript, to explore an original class of active worms, denoted to as Camouflaging Worm (Abbreviated to as C-Worm) respectively. The C-Worm means different from traditional worms because of its ability to intelligently manipulate its scan traffic size over time respectively. So, the C-Worm camouflages its circulation from prevailing worm detection systems constructed on investigating the circulation traffic produced by worms respectively. To analyze characteristics of the C- Worm and conduct a comprehensive comparison between its non-worm traffic and traffic respectively. To detect that these double kinds of traffic are barely different in the time sphere. Though, their difference is clear in the incidence sphere, due to the periodic devious environment of the C-Worm respectively. Interested by these clarifications, it designs a novel spectrum-constructed pattern to detect the C-Worm. This pattern consumes the Power Spectral Density (PSD) circulation of the scan traffic size and its corresponding Spectral Flatness Measure (SFM) respectively, to distinguish the C-Worm traffic from related traffic. Consuming a complete group of detection metrics and real-world suggestions as contextual traffic, it directs widespread implementation estimates on these planned spectrumconstructed detection pattern. This implementation data evidently establishes that this pattern can efficiently detect the C-Worm circulation. Besides, to display the simplification of this spectrum-based pattern in efficiently detecting not only the C-Worm, although conventional worms also.


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How to Cite

Ravichandran, S., & Umamaheswari, M. (2016). Design and Development of Humiliating of C-Worm Utilizing an Arbitrary Visualizing. Asian Journal of Computer Science and Technology, 5(1), 1–5.

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