A Machine Learning Framework with Feature Selection and Hyperparameter Tuning Optimizations for Intrusion Detection
Keywords:
Artificial Intelligence, Machine Learning, Intrusion DetectionAbstract
The recent increase in cyber-attacks has made cyber security upgrading an on-going task. Heuristics were the foundation of traditional security systems; they were designed to identify intrusions depending on how they were detected. But as artificial intelligence (AI) techniques like machine learning (ML) have become more popular, learning-based models have shown to be effective because of their capacity to constantly learn from tagged data. The research indicates that when training samples are not of the intended amount and quality, supervised learning-based machine learning models perform worse at detecting intrusions. Utilizing the performance of ML models with certain tweaks is crucial. The paper, which intends to create an intrusion detection system based on machine learning and feature engineering, is motivated by this. We proposed two algorithms named Hybrid Feature Selection (HFS) and Learning based Intrusion Detection (LbID). We evaluate the system with the CICIDS2017 dataset. Binomial and multi-class classification is applied in the implementation of intrusion detection systems. With 94.22% accuracy, the RF model has the best binomial classification accuracy. Decision Tree has the greatest accuracy (91.67%) when it comes to multi-class classification without optimizations. RF exhibits the maximum accuracy of 93.46% in the case of multi-class classification with optimizations.
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