Towards a Novel Intrusion Detection Architecture using Artificial Intelligence
Pages 185 - 189
Abstract
Artificial intelligence (AI) is a transformative technology for potential replacement of human tasks and activities within industrial, social, intellectual, and digital applications. Network intrusion detection is crucial to identify cyber-attacks in critical infrastructures where a dynamic collection and analysis of network traffic can be conducted using AI. In this research paper we develop a novel intrusion detection architecture to mitigate malicious traffic passing through cyber infrastructure of an organization. We propose to design scenarios based on AI for intelligent self-protection or alert system that will facilitate countering actual cyber-attacks. The system will utilize machine learning algorithm - Random Forest - to offer more flexibility to discover new attacks and to ensure training the system to predict them in the future. Moreover, we design spam filtering program on python to detect spam emails as per email is one of the main attacking vectors that threatens the security of critical infrastructures.
References
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Samrin, R., & Vasumathi, D. (2017, December). Review on anomaly based network intrusion detection system. In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT) (pp. 141--147). IEEE.
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Liu, J., Zhang, W., Tang, Z., Xie, Y., Ma, T., Zhang, J., ... & Niyoyita, J. P. (2020). Adaptive intrusion detection via GAGOGMM-based pattern learning with fuzzy rough set-based attribute selection. Expert Systems with Applications, 139, 112845.
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Ren, J., Guo, J., Qian, W., Yuan, H., Hao, X., & Jingjing, H. (2019). Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms. Security and Communication Networks, 2019.
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Index Terms
- Towards a Novel Intrusion Detection Architecture using Artificial Intelligence
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Published In
November 2020
251 pages
ISBN:9781450377218
DOI:10.1145/3436829
Copyright © 2020 ACM.
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- Ain Shams University: Ain Shams University, Egypt
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 January 2021
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- Research-article
- Research
- Refereed limited
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ICSIE 2020
ICSIE 2020: 2020 9th International Conference on Software and Information Engineering
November 11 - 13, 2020
Cairo, Egypt
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