@article{MAKHILLAJIT201615186398,
    title = {An Enhanced Hidden Markov Dynamic Bayesian model for Resisting Camouflaging Worm attack study},
    journal = {Asian Journal of Information Technology},
    volume = {15},
    number = {18},
    pages = {3616-3623},
    year = {2016},
    issn = {1682-3915},
    doi = {ajit.2016.3616.3623},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2016.3616.3623},
    author = {R. and},
    keywords = {Camouflaging worm attack internet services,spectral density,hidden markov chain,bayesian network,C-worm},
    abstract = {At present, Camouflaging worm attack constitute a large part of internet peer servers. Due to the
increasing traffic in internet services, it has become inevitable to take into account its effects on network
management. Generally, studies on resisting Camouflaging Worm attack have involved analysis with power
spectral density distribution via spectrum-based scheme. However, with several facilities provided by spectrumbased
scheme, its network traffic volume in internet severs is increasing day by day increasing the malicious
traffic rate. In this research proposal plan is to develop efficient identification of C-Worm propagation and
restriction of uncontrolled malicious traffic in the internet by applying Enhanced Hidden Markov Chain-based
C-Worm Detection (EHMC-CWD) technique. The C-Worm replicates the abnormal traffic on its own and
propagates throughout the network and cause damages to the internet services. Enhanced Hidden Markov
Chain (EHMC) identifies the camouflaging abnormal traffic replicated across the internet. Next, EHMC adapted
a dynamic Bayesian network to evaluate camouflaging worm propagation by means of optimal non linear
filtering. Therefore the replicated traffic generated by C-Worm reveals the information about the sequence of
traffic in which it is propagated. The performance of EHMC-CWD is evaluated by extensive simulations.
Simulation results show that our proposal can considerably reduce the execution time for C-Worm detection
and memory space and also improves high detection rate to a certain degree.}
    }