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This analysis aims to evaluate machine learning models for enhancing the performance of intrusion detection systems (IDS).

January 20, 2022

Empirical Analysis of Machine Learning Models towards Adaptive Network Intrusion Detection Systems

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This analysis aims to evaluate machine learning models for enhancing the performance of intrusion detection systems (IDS).
Conducted a review of existing machine learning and deep learning algorithms used in intrusion detection systems.
Assessed various datasets and evaluation metrics to better understand their effectiveness.
Analyzed challenges and future prospects in the IDS domain based on current research findings.
Identified improved detection accuracy and reduced false alarm rates using machine learning-based IDS.
Highlighted the effectiveness of hybrid and ensemble classifiers in handling diverse datasets.
Provided a roadmap for future research directions in network intrusion detection.

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Review of machine learning approaches for improving network intrusion detection effectiveness and challenges.

Senthilkumar et al. (2022) studied this question.

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