Polymorphic malware has evolved as a major threat in Computer Systems. Their creation technology is constantly evolving using sophisticated tactics to create multiple instances of the existing ones. Current solutions are not yet able to sufficiently address this problem. They are mostly signature based; however, a changing malware means a changing signature. They, therefore, easily evade detection. Classifying them into their respective families is also hard, thus making elimination harder. In this paper, we propose a new feature engineering (NFE) approach for a better classification of polymorphic malware based on a hybrid of structural and behavioural features. We use accuracy, recall, precision, and F score to evaluate our approach. We achieve an improvement of 12% on accuracy between raw features and NFE features. We also demonstrated the robustness of NFE on feature selection as compared to other feature selection techniques.
Growing research in computer vision obliges reliable and efficient target tracking methods. This letter proposes a review on target estimation and tracking algorithms grouped into two namely deterministic and nondeterministic based algorithms. Various issues affecting success and/or failure of such algorithms are discussed as well as presenting current references (fifty five in total) on the topic. In conclusion, no single algorithm can be ideal for all tracking problems. It is hoped that with only two groups of tracking and estimation algorithms, this work will guide tracking system designers on choosing a suitable algorithms for use.
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