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Comparative analysis of reduced feature set based ML models for IDS
Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine L...
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| creator | Jishiya, P. A. Swaraj, K. P. James, Ajay |
| description | Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine Learning for developing an IDS can make it more powerful. This work aims at evaluating the performance of machine learning models for Intrusion Detection System built based on CART Algorithm, ID3Decision tree algorithm, Random forest algorithm with Convolutional Neural Network. The model is built using KDD Cup99 Dataset. In this work we perform Linear Correlation and Mutual Information feature selection algorithms for ML algorithms and for Convolutional Neural Network algorithm being a deep learning technique feature selection happens automatically. Analysing various parameters including F1-score, ID3 has shown better results. |
| doi_str_mv | 10.1063/5.0138520 |
| format | conference_proceeding |
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A. ; Swaraj, K. P. ; James, Ajay</creator><contributor>Jaseena, K U ; Manzur Ali, P P ; Jasmine, P M</contributor><creatorcontrib>Jishiya, P. A. ; Swaraj, K. P. ; James, Ajay ; Jaseena, K U ; Manzur Ali, P P ; Jasmine, P M</creatorcontrib><description>Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine Learning for developing an IDS can make it more powerful. This work aims at evaluating the performance of machine learning models for Intrusion Detection System built based on CART Algorithm, ID3Decision tree algorithm, Random forest algorithm with Convolutional Neural Network. The model is built using KDD Cup99 Dataset. 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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP Conference Proceedings, 2023, Vol.2773 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_proquest_journals_2817827878 |
| source | American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list) |
| subjects | Algorithms Anomalies Artificial neural networks Cybersecurity Deep learning Intrusion detection systems Machine learning |
| title | Comparative analysis of reduced feature set based ML models for IDS |
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