We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An Ensemble-Based Machine Learning Approach for Cyber-Attacks Detection in Wireless Sensor Networks.
- Authors
Ismail, Shereen; El Mrabet, Zakaria; Reza, Hassan
- Abstract
Wireless Sensor Networks (WSNs) are the key underlying technology of the Internet of Things (IoT); however, these networks are energy constrained. Security has become a major challenge with the significant increase in deployed sensors, necessitating effective detection and mitigation approaches. Machine learning (ML) is one of the most effective methods for building cyber-attack detection systems. This paper presents a lightweight ensemble-based ML approach, Weighted Score Selector (WSS), for detecting cyber-attacks in WSNs. The proposed approach is implemented using a blend of supervised ML classifiers, in which the most effective classifier is promoted dynamically for the detection process to gain higher detection performance quickly. We compared the performance of the proposed approach to three classical ensemble techniques: Boosting-based, Bagging-based, and Stacking-based. The performance comparison was conducted in terms of accuracy, probability of false alarm, probability of detection, probability of misdetection, model size, processing time, and average prediction time per sample. We applied two independent feature selection techniques. We utilized the simulation-based labeled dataset, WSN-DS, that comprises samples of four internal network-layer Denial of Service attack types: Grayhole, Blackhole, Flooding, and TDMA scheduling, in addition to normal traffic. The simulation revealed promising results for our proposed approach.
- Subjects
WIRELESS sensor networks; DENIAL of service attacks; FEATURE selection; INTERNET of things; CYBERTERRORISM; MACHINE learning
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 1, p30
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13010030