A Perceptive Analysis of Machine Learning Techniques for Enhancing Cybersecurity Intrusion Detection Systems

This paper is composed of a literature review discussing the methods of improving Intrusion Detection Systems (IDS) using the UNSW-NB15 dataset to predict intrusion. The traditional IDS has the disadvantage of having too many false positives in detecting new threats. Supervised algorithms, including the Random Forest, performed well of 95.2 to eradicate all 0-day attacks and 85% of unsupervised autoencoders, as compared to the composite of the supervised and unsupervised encoders with a score of 94.8. False positives decreased to 4.2, and it supported high-rate operations at the network. Therefore, the datasets cannot be effortlessly represented, and even some tasks can be computed, although the situation has been improved. This study provides a sound ML-based IDS model that is more precise and versatile and has the potential for direct effects with regard to the implementation of cybersecurity in the real world.

Analysis, Machine Learning, Cybersecurity, Detection

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