Abstract
Nowadays, security researchers witness an exponential growth of the number of malware variants in the wild. On top of this, various advanced techniques like metamorphism, server-side polymorphism, anti-emulation, commercial or custom packing, and so on, are being used in order to evade detection. It is clear that standard detection techniques no longer cope with the ongoing anti-malware fight. This is why machine learning techniques for malware detection are continually being developed and improved. These, however, operate on huge amounts of data and face challenges like finding an equilibrium between the three most desired requirements: low false positive rate, high detection rate, acceptable performance impact. This paper aims to reach this equilibrium by starting with an algorithm which has a zero false positive rate during the training phase and continuing by further improving it, in order to increase the detection rate without significantly altering the low false positive property.
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Vatamanu, C., Cosovan, D., Gavriluţ, D., Luchian, H. (2017). Perceptron-Based Ensembles and Binary Decision Trees for Malware Detection. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_29
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DOI: https://doi.org/10.1007/978-3-319-68612-7_29
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