Abstract
This paper presents a Genetic Programming approach to feature extraction in the frame of the perceptron algorithm described in [1]. While feature extraction has the potential of increasing the accuracy of classification, fully exploring the huge space of possible combinations of the initial 45150 features would make the approach infeasible; Genetic Programming provides a proper way of tackling the search for relevant features. In turn, the extracted features are used to train an algorithm - One Side Class Perceptron - designed to minimize the number of false positives; accuracy is increased. In the experiments, the classifier using the extracted features was run on a dataset consisting of 358,144 files. The results show that our overall approach and implementation is fit for real-world malware detection.
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Benchea, R., Gavrilut, D., Luchian, H. (2018). Feature Creation Using Genetic Algorithms for Zero False Positive Malware Classification. In: Tantar, AA., Tantar, E., Emmerich, M., Legrand, P., Alboaie, L., Luchian, H. (eds) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI. Advances in Intelligent Systems and Computing, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-69710-9_6
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DOI: https://doi.org/10.1007/978-3-319-69710-9_6
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