Abstract
In the current digital era, the development and advancement of various technologies such as cloud computing, edge computing, the Internet of Things, etc., have benefited users and organizations in numerous ways. Though several security mechanisms are used to protect computing systems and networks from attacks, they are insufficient and lack the capability to handle new attacks developed at a breakneck pace. Machine learning poses a potential solution. However, its vulnerability to attacks by adversaries can cause severe attacks to escape the detection process and gain access to the network. To build a robust intrusion detection system, in this paper, we have experimented on the NSL-KDD dataset and used different machine learning techniques to identify and detect the attacks in it and in the adversarial test dataset that contains attacks generated with attack generation methods. The performance of the machine learning techniques reduces drastically with the adversarial data, making it necessary to implement a defense strategy against the attacks.
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Mulimani, M., Rachh, R., Kavatagi, S. (2023). A Comparative Approach: Machine Learning and Adversarial Learning for Intrusion Detection. In: Yadav, R.P., Nanda, S.J., Rana, P.S., Lim, MH. (eds) Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-8742-7_39
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DOI: https://doi.org/10.1007/978-981-19-8742-7_39
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