| Title | A Review on Intrusion Detection System Using Machine Learning Techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Musa, Usman Shuaibu, Chakraborty, Sudeshna, Abdullahi, Muhammad M., Maini, Tarun |
| Conference Name | 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) |
| Keywords | Classification algorithms, Ensemble, hybrid, Intrusion detection, intrusion detection system, machine learning, machine learning algorithms, Metrics, misuse detection, Neural networks, Predictive models, predictive security metrics, pubcrawl, security, Single classifiers |
| Abstract | Computer networks are exposed to cyber related attacks due to the common usage of internet, as the result of such, several intrusion detection systems (IDSs) were proposed by several researchers. Among key research issues in securing network is detecting intrusions. It helps to recognize unauthorized usage and attacks as a measure to ensure the secure the network's security. Various approaches have been proposed to determine the most effective features and hence enhance the efficiency of intrusion detection systems, the methods include, machine learning-based (ML), Bayesian based algorithm, nature inspired meta-heuristic techniques, swarm smart algorithm, and Markov neural network. Over years, the various works being carried out were evaluated on different datasets. This paper presents a thorough review on various research articles that employed single, hybrid and ensemble classification algorithms. The results metrics, shortcomings and datasets used by the studied articles in the development of IDS were compared. A future direction for potential researches is also given. |
| DOI | 10.1109/ICCCIS51004.2021.9397121 |
| Citation Key | musa_review_2021 |
A Review on Intrusion Detection System Using Machine Learning Techniques