Abstract
Cybersecurity threats have increased dramatically in recent years, and the techniques used by the attackers continue to evolve and become more ingenious. These attacks harm organizations on many levels, such as economic, reputational, and legal. Therefore, an Anomaly Detection Based Network Intrusion Detection System (ADNIDS) is an essential component of any standard security framework in computer networks. In this paper, we propose two deep learning-based models, BDNN and MDNN, for binary and multiclass classification of network attacks, respectively. We evaluate the performance of our proposed models on the well-known NSL-KDD dataset and compare our results with similar deep-learning approaches and state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall.
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Acknowledgment
This research project was supported by a grant from the Research Center of the Female Scientific and Medical Colleges, the Deanship of Scientific Research, King Saud University.
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Altwaijry, N., ALQahtani, A., AlTuraiki, I. (2020). A Deep Learning Approach for Anomaly-Based Network Intrusion Detection. In: Tian, Y., Ma, T., Khan, M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_46
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DOI: https://doi.org/10.1007/978-981-15-7530-3_46
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