Machine learning based intrusion detection system: an experimental comparison

Imran Hidayat*, Muhammad Zulfiqar Ali, Arshad Arshad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Recently, networks are moving towards automation and getting more and more intelligent. With the advent of big data and cloud computing technologies, lots and lots of data is being produced on the internet. Every day petabytes of data are produced from websites, social media sites, or the internet. As more and more data are produced, there is a continuous threat of network attacks also growing. An intrusion Detection System(IDS) is used to detect such types of attacks in the network. IDS inspects packet headers and data and decides whether the traffic is anomalous or normal based on the contents of the packet. In this research, ML techniques are being used for intrusion detection purposes. Feature selection is also used for efficient and optimal feature selection. The research proposes a hybrid feature selection technique composed of the Pearson Correlation Coefficient and Random Forest Model. For the Machine Learning Decision tree, AdaBoost and KNN are trained and tested on the TON_IOT Dataset. The Dataset is new and contains new and recent attack types and features. For Deep Learning (DL), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are trained and tested. Evaluation is done on the basis of accuracy, precision, and recall. It is concluded from the results that the Decision tree for ML and MLP for DL provides optimal accuracy with fewer false positive and negative rates. It is also concluded from the results that the ML techniques are effective for detecting intrusion in the networks.
Original languageEnglish
JournalJournal of Computational and Cognitive Engineering
Publication statusAccepted/In press - 11 Jul 2022

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