Advanced Machine Learning Methodologies For Intelligent Cybersecurity Intrusion Detection Systems
DOI:
https://doi.org/10.29284/tgj36d31Keywords:
Machine Learning, Intrusion Detection Systems, Cybersecurity, Anomaly Detection, Hybrid Learning Models.Abstract
This paper compiles MLS applications in both the subject of Intrusion Detection Systems (IDS); it compares shortcomings of the yesteryear rule-based systems, which comprise excessive false alarms and transformation to counter new threats. It contrasts supervised (e.g., Random Forests) with unsupervised (e.g., Autoencoders) and reinforcement learning (e.g., Deep Q-Networks) where each of them can be useful in accuracy and flexibility. They include the issues, the imbalance of the dataset, the adversarial attacks, and the complexity of the computation, but one of the available datasets is UNSW-NB15. The zero-day detection and the federated learning will be the planned trends in the future. It is also advised that hybrid types and frameworks that can be comprehended will be required to enhance the strength and scalability of IDS in combating the emerging cyber threats at the time of this externality.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
