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- Title
Optimizing Intrusion Detection in Edge Computing Network: A Hybrid ML Approach with Recursive Feature Elimination.
- Authors
Kumar, Amit; Kumar, Vivek; Singh Bhadauria, Abhay Pratap
- Abstract
As the prevalence of Internet of Things (IoT) devices increases, Cyber incidents are also increasing significantly. These Cyber incidents are mainly caused by various attacks, such as Distributed Denial of Service (DDoS), Denial of Service (DoS), intrusions, and web-based attacks. This type of attacks can severely impact valuable IoT system resources, compromise stored data, and lead to substantial financial losses if not adequately mitigated. Detecting these attacks within network traffic is complex and requires intelligent Intrusion Detection Systems (IDS). This paper proposes a Machine Learning (ML) based hybrid IDS model for edge computing networks. The feature selection process employs the 'Recursive Feature Elimination technique' (RFE) combined with 'Random Forest' (RF) to identify optimal features for attack detection. The Hybrid IDS model integrates 'Random Forest' (RF), 'Decision Tree' (DT), 'Extra Tree' (ET), and 'K-Nearest Neighbor' (KNN) algorithms. The Hybrid IDS model is evaluated on four datasets: 'CIC-IDS-2017', 'NSL-KDD', 'UNSW-NB15', and 'CSE-CIC-IDS-2018'. The results of the proposed model show maximum prediction accuracy of 99.92%, 99.89%, 99.50%, and 99.13%, and F1-score values obtained are 99.95%, 99.90%, 99.23%, and 99.13% on 'CIC-IDS-2017', 'NSL-KDD', 'UNSW-NB15', and 'CSE-CIC-IDS2018' datasets, respectively. The experimental results clearly demonstrate that the proposed model performs better than the models reported in the existing studies.
- Subjects
COMPUTER network traffic; FEATURE selection; RANDOM forest algorithms; EDGE computing; K-nearest neighbor classification; INTRUSION detection systems (Computer security)
- Publication
International Journal of Intelligent Engineering & Systems, 2025, Vol 18, Issue 1, p36
- ISSN
2185-310X
- Publication type
Academic Journal
- DOI
10.22266/ijies2025.0229.04