This work presents an automatic methodology able to improve ma-chine-generated signatures for Android Malware detection. The technique relies on a population-less evolutionary algorithm and uses an unorthodox fitness function that incorporates unsystematic human experts knowledge in the form of a set of rules of thumb. The proposed optimization algorithm does not require to rank the individuals, as exploiting experts knowledge, the resulting population of candidate solutions is not a totally ordered set any more. Experimental results show that the resulting signatures are of good quality and more accurate than the original ones, lowering both false positives and negatives.

Evolutionary Antivirus Signature Optimization / Giovannitti, Eliana; Mannella, Luca; Andrea, Marcelli; Squillero, Giovanni. - STAMPA. - (2019), pp. 905-912. ((Intervento presentato al convegno 2019 IEEE Congress on Evolutionary Computation (CEC) tenutosi a Wellington nel 10-13 June 2019 [10.1109/CEC.2019.8790240].

Evolutionary Antivirus Signature Optimization

GIOVANNITTI, ELIANA;Luca Mannella;Giovanni Squillero
2019

Abstract

This work presents an automatic methodology able to improve ma-chine-generated signatures for Android Malware detection. The technique relies on a population-less evolutionary algorithm and uses an unorthodox fitness function that incorporates unsystematic human experts knowledge in the form of a set of rules of thumb. The proposed optimization algorithm does not require to rank the individuals, as exploiting experts knowledge, the resulting population of candidate solutions is not a totally ordered set any more. Experimental results show that the resulting signatures are of good quality and more accurate than the original ones, lowering both false positives and negatives.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2743672