Current anti-virus techniques include signature based detection, anomaly based detection, and machine learning based virus detection. Signature detection is the most widely used approach. Metamorphic malware changes its internal structure with each infection. Metamorphism provides one of the strong known methods for evading malware detection. In this project, we consider metamorphic virus detection based on a directed graph obtained from executable files. We compare our detection results with a previously developed and highly successful technique based on hidden Markov models. v
Numerous applications require effectual data representation as well as reprocessing in signal processing. For effectual signal representation, the compression technique is exploited which is considered as the standard model. Nowadays, numerous novel approaches are adopted at the sensing level for compression. One of the growing domains is compressed sensing which is based on the revelation that is a minimum congregation of a linear projection of sparse signal's such as sufficient information for renovation. By means of Compressed Sensing, the signal sampling is allowed at a rate under the rate of Nyquist sampling when relies on the sparsity of the signal. Moreover, the original signal reconstruction from few compressive measurements is authentically used for the deviated reconstruction Compressed Sensing approaches. The main objective of this work is to propose a novel compressive sensing technique for signal reconstruction in biomedical data. Hence, using three phases the signal is compressed such as stable measurement matrix design, signal compression as well as reconstruction of the signal. Here, the compression phase involves a novel operational technique that comes first with three operations. Moreover, here, evaluation of Θ and normalization as well as signal transformation is performed. This work exploits the Haar wavelet matrix model to calculate the theta (Θ) value. Furthermore, this work assures the superiority of the developed model exploiting the optimization idea with the assessment process. By exploiting a novel optimization method named Self adaptive Salp Swarm Algorithm (SSA), is to optimally select the Haar wavelet function vector coefficient.
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