Intrusion Detection System: A Comparative Study of Machine Learning-based IDS
Amit Kumar Singh, Jay Prakash, Gaurav Kumar · Research Square · 2022
Abstract Due to the Covid-19 pandemic, there has been a significant rise in the amount of data processed and transferred to any communication network. The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for Intrusion Detection Systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, we have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and ten machine learning classifiers. It has been observed that the top three IDS models KNeighbors, XGBoost and AdaBoost outperform in binary-class classification with 99.49%, 99.14% and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88% and 96.66% accuracy, respectively.