Network Anomaly Detection System using Deep Learning with Feature Selection Through PSO
Rimjhim Rathore1, Neeraj Shrivastava2
1Rimjhim Rathore, Department of Computer Science and Engineering, IES, IPS Academy Indore, Indore (M.P), India.
2Dr. Neeraj Shrivastava, Head of Department, Department of Computer Science and Engineering, IES, IPS Academy Indore, Indore (M.P), India.
Manuscript received on 19 April 2022 | Revised Manuscript received on 27 March 2023 | Manuscript Accepted on 15 April 2023 | Manuscript published on 30 April 2023 | PP: 1-6 | Volume-11 Issue-5, April 2023 | Retrieval Number: 100.1/ijese.F25310510622 | DOI: 10.35940/ijese.F2531.0411523
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The more computer systems that communicate and cooperate, the more crucial it is to make our lives simpler. At the same time, it highlights faults that people are unable to correct. Due to faults, cyber-security procedures are required to communicate data. Secure communication requires both the installation of security measures and the development of security measures to address changing security concerns. In this study, it is suggested that network intrusion detection systems be able to adapt and be resilient. This could be done by using deep learning architectures. Deep learning is used in this article to find and group network attacks. There are some tools that can help intrusion detection systems that are more flexible learn to recognise new or zero-day network behaviour features, which can help them get rid of bad guys and make it less likely that they’ll get into your network. The model’s efficacy was tested using the KDD dataset, which combines real-world network traffic with fake attack operations.
Keywords: Intrusion Detection System, KDD, Deep Learning, Accuracy, Cyber-Security.
Scope of the Article: Deep Learning