Abstract
In the Internet age, malicious software (malware) represents a serious threat to the security of information systems. Malware-detection systems to protect computers must perform a real-time analysis of the executable files. The paper shows that machine-learning methods can support the challenging, yet critical, task of unseen malware recognition, i.e., the classification of malware variants that were not included in the training set. The experimental verification involved a publicly available dataset, and confirmed the effectiveness of the overall approach.
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Bisio, F., Gastaldo, P., Meda, C., Nasta, S., Zunino, R. (2016). Machine Learning-Based System for Detecting Unseen Malicious Software. In: De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. Lecture Notes in Electrical Engineering, vol 351. Springer, Cham. https://doi.org/10.1007/978-3-319-20227-3_2
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DOI: https://doi.org/10.1007/978-3-319-20227-3_2
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