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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References
Gavrilut, D., Benchea, R., Vatamanu, C.: Optimized zero false positives perceptron training for malware detection. In: SYNASC, pp. 247–253 (2012)
Leather, H., Bonilla, E., O’Boyle, M.: Automatic feature generation for machine learning based optimizing compilation. In: Proceedings of the 7th Annual IEEE/ACM International Symposium on Code Generation and Optimization, CGO 2009, Washington, DC, USA, pp. 81–91. IEEE Computer Society (2009)
Guo, H., Jack, L., Nandi, A.: Feature generation using genetic programming with application to fault classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35(1), 89–99 (2005)
Kowaliw, T., Banzhaf, W., Kharma, N., Harding, S.: Evolving novel image features using genetic programming-based image transforms. In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 2502–2507, May 2009
Pei, M., Goodman, E.D., Punch, W.F.: Feature extraction using genetic algorithms. In: Proceedings of International Symposium on Intelligent Data Engineering and Learning’98 (IDEAL98), Hong Kong, p. 98 (1997)
Punch, W., Goodman, E., Pei, M., Chia-Shun, L., Hovland, P., Enbody, R.: Further research on feature selection and classification using genetic algorithms (1993)
Sherrah, J.R., Bogner, R.E., Bouzerdoum, A.: The evolutionary pre-processor: automatic feature extraction for supervised classification using genetic programming. In: Koza, J.R., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M., Iba, H.,Riolo, R.L., (eds.) Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, pp. 304–312. Morgan Kaufmann (1997)
Ritthoff, O., Klinkenberg, R., Fischer, S., Mierswa, I.: A hybrid approach to feature selection and generation using an evolutionary algorithm. In: Proceedings of 2002 U.K. Workshop on Computational Intelligence (UKCI-02), pp. 147–154 (2002)
Shafiq, M.Z., Tabish, S.M., Farooq, M.: On the appropriateness of evolutionary rule learning algorithms for malware detection. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2609–2616. ACM (2009)
Rafique, M.Z., Chen, P., Huygens, C., Joosen, W.: Evolutionary algorithms for classification of malware families through different network behaviors. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO). ACM Press (2014). To appear
Mehdi, S.B., Tanwani, A.K., Farooq, M.: IMAD: in-execution malware analysis and detection. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1553–1560. ACM (2009)
Edge, K.S., Lamont, G.B., Raines, R.A.: A retrovirus inspired algorithm for virus detection and optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, New York, NY, USA, pp. 103–110. ACM Press (2006)
Loong, S.N.K., Mishra, S.K.: De Novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinform. Comput. Appl. Biosci. 23, 1321–1330 (2007)
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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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