Deep learning applications on cybersecurity: a practical approach
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Fecha
2024-01-01
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Elsevier B.V.
Resumen
One of the most difficult challenges for computer systems has been security. On the other hand, new developments in machine learning are having an impact on almost every aspect of computer science, including cybersecurity. To analyze this impact, we have created three distinct cybersecurity-related problems to show the advantages of deep learning techniques. We examined their potential applications for SPAM filtering, detecting malicious software, and adult-content detection. We experimented with various techniques, such as Long Short-Term Memory (LSTMs) for spam filtering, Deep Neural Networks (DNNs) for malware detection, Convolutional Neural Networks (CNNs) combined with Transfer Learning for adult content detection and image augmentation methods. We are able to achieve an Area Under ROC Curve greater than 0.94 in every scenario, proving that excellent performance with a good relation between cost and effectiveness may be created without the need of complex designs.
Palabras clave
Cybersecurity
Deep learning
Image classification
NLP
Transfer learning
Deep learning
Image classification
NLP
Transfer learning
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Miranda-García, A., Rego, A. Z., Pastor-López, I., Sanz, B., Tellaeche, A., Gaviria, J., & Bringas, P. G. (2024). Deep learning applications on cybersecurity: a practical approach. Neurocomputing, 563. https://doi.org/10.1016/J.NEUCOM.2023.126904