ADAPTIVE INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING IN CLOUD ENVIRONMENTS
DOI:
https://doi.org/10.29121/shodhkosh.v4.i1.2023.3358Keywords:
Intrusion Detection Systems, Machine Learning, Cloud Security, Adaptive Systems, Anomaly Detection, Real-Time Threat Mitigation, Adversarial AttacksAbstract [English]
There are considerable hurdles that must be overcome in order to maintain adequate security against cyber-attacks in cloud settings because of their dynamic and dispersed nature. A proactive method to identifying and mitigating new threats in real time is provided by adaptive intrusion detection systems (IDS) that make use of machine learning (ML). The design and operation of adaptive intrusion detection systems (IDS) that are adapted for cloud platforms are investigated in this study. Particular attention is paid to the utilisation of supervised, unsupervised, and reinforcement learning methods. It investigates the benefits of adaptive intrusion detection systems (IDS) in terms of identifying abnormal behaviours, minimising the number of false positives, and adapting to shifting threat environments. Several important factors, including the selection of features, the quality of the dataset, and the optimisation of the algorithm, are covered. In addition to this, we investigate the possibility of integrating adaptive intrusion detection systems with cloud-native technologies such as serverless computing and containers. Performance measurements and comparative evaluations demonstrate that machine learning-based intrusion detection systems are more effective than older techniques. Additionally, the study addresses issues such as scalability, data privacy, and adversarial assaults, and it proposes viable methods to improve dependability. The adaptive intrusion detection system (IDS) is an essential component of cloud security methods since it enables continuous monitoring and reaction to complex kinds of attacks.
References
Ahmed, E., & Maher, G. (2024). Optimizing Supply Chain Logistics with Big Data and AI: Applications for Reducing Food Waste. Journal of Current Science and Research Review, 2(02), 29-39.
Gerges, M., & Elgalb, A. (2024). Comprehensive Comparative Analysis of Mobile Apps Development Approaches. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 430-437. DOI: https://doi.org/10.60087/jaigs.v6i1.269
Gerges, M., Elgalb, A., & Freek, A. (2024). Concealed Object Detection and Localization in Millimetre Wave Passengers’ Scans. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4), 372-382. DOI: https://doi.org/10.60087/jklst.v3.n4.p372
Zolotan, M., & Ross, A. (2016). Intrusion Detection Systems in Cloud Computing: A Survey. International Journal of Computer Applications, 143(10), 1-5.
S. Pal, S. Khatua, N. Chaki, and S. Sanyal, “A New Trusted and Collaborative Agent Based Approach for Ensuring Cloud Security”, International Journal of Engineering 2012.
Zhe Li, Weiqing Sun, Lingfeng Wang, “A Neural Network Based Distributed Intrusion Detection System On Cloud Platform”, IEEE 2012. DOI: https://doi.org/10.1109/CCIS.2012.6664371
Changsheng Xiang , Zhou Yu , Xilong Qu, “Support Vector Machine Optimized by Improved Genetic Algorithm” Telkomnika Indonesian Journal of Electrical Engineering 2014 DOI: https://doi.org/10.11591/telkomnika.v12i1.3182
Preeti Mishra, Emmanuel S. Pilli , Vijay Varadharajan, Udaya Tupakula, “Nucleoids: A Security Architecture to Detect Intrusions at Network and Virtualization Layer in Cloud Environment”, Conference on Advances in Computing, Communications and Informatics 2016. DOI: https://doi.org/10.1109/ICACCI.2016.7732025
Preeti Mishra, Emmanuel S. Pilli, Vijay Varadharajan,Udaya Tupakula, “Efficient Approaches for Intrusion Detection in Cloud Environment”, International Conference on Computing, Communication and Automation (ICCCA2016). DOI: https://doi.org/10.1109/CCAA.2016.7813926
Kleber, schulter, “Intrusion Detection for Grid and Cloud Computing”, IEEE Journal: IT Professional, 19 July 2010.
Modi, C., Patel, D., Patel, H., Borisaniya, B., Patel, A. and Rajarajan, M. (2013). A survey of intrusion detection techniques in Cloud. Journal of Network and Computer Applications, 36(1), pp. 42-57. doi: 10.1016/j.jnca.2012.05.003. DOI: https://doi.org/10.1016/j.jnca.2012.05.003
Chirag Modi, Dhiren Patel, Bhavesh Borisanya, Avi Patel, and Muttukrishnan Rajarajan. A novel framework for intrusion detection in cloud. In Proceedings of the Fifth International Conference on Security of Information and Networks, pages 67– 74. ACM, 2012. DOI: https://doi.org/10.1145/2388576.2388585
K.Deepa, and M.Chatterjee. "An adaptive distributed intrusion detection system for cloud computing framework." In Recent Trends in Computer Networks and Distributed Systems Security, pp 466-473, Springer, Berlin, Heidelberg, 2012. DOI: https://doi.org/10.1007/978-3-642-34135-9_45
Marwane Zekri, Said El Kafhali, Noureddine Aboutabit and Youssef Saadi. “DDoS attack detection using machine learning techniques in cloud computing environments.” Conference: 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), 2017. DOI: https://doi.org/10.1109/CloudTech.2017.8284731
Amar Amouri, Vishwa T. Alaparthy and Salvatore D. Morgera. “A Machine Learning Based Intrusion Detection System for Mobile Internet of Things.” Advanced Intrusion Detection & Mitigation Systems in Wireless Sensor Networks, Sensors 2020, 20(2), 461. DOI: https://doi.org/10.3390/s20020461
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ravindrakumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
