Tripathi, Gaurav and Singh, Vishal Krishna and Sharma, Varun and Vinodbhai, Majithia Vivek (2024) Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks. IEEE Access, 12. pp. 20973-20982. DOI https://doi.org/10.1109/access.2024.3362794
Tripathi, Gaurav and Singh, Vishal Krishna and Sharma, Varun and Vinodbhai, Majithia Vivek (2024) Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks. IEEE Access, 12. pp. 20973-20982. DOI https://doi.org/10.1109/access.2024.3362794
Tripathi, Gaurav and Singh, Vishal Krishna and Sharma, Varun and Vinodbhai, Majithia Vivek (2024) Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks. IEEE Access, 12. pp. 20973-20982. DOI https://doi.org/10.1109/access.2024.3362794
Abstract
Network intrusion detection systems work on huge data sets, with large feature sets dominated by noisy data and irrelevant features, resulting in steep degradation in detection accuracy and a steep proliferation in model training and computation time. This work presents a novel method to optimize the feature selection process in machine learning algorithms for accurate detection of intrusion attacks in communication networks. The proposed method targets features with a high impact on the target variable to optimize feature selection and reduction. The CICIDS-2017 data set is used to test the performance of the proposed approach. Results prove the dexterity of the proposed method as it is able to achieve an almost 51% reduction in irrelevant features and increases the detection accuracy of the tuned random forest classifier to 99.9% with an almost 50% reduced model computation time.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Communication networks; machine learning; random forest; intrusion detection; network attacks |
Subjects: | Z Bibliography. Library Science. Information Resources > ZZ OA Fund (articles) |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Mar 2024 13:52 |
Last Modified: | 16 May 2024 22:16 |
URI: | http://repository.essex.ac.uk/id/eprint/37772 |
Available files
Filename: Weighted_Feature_Selection_for_Machine_Learning_Based_Accurate_Intrusion_Detection_in_Communication_Networks.pdf
Licence: Creative Commons: Attribution 4.0