Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Sensors

卷:21

Waleed Abu-Ain¹  Raniyah Wazirali²  Tarik Abu-Ain²  Qusay Bsoul³  Rami Ahmad⁴ 

[1] College of Community, Taibah University, Badr 46354, Saudi Arabia;

[2] College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia;

[3] Faculty of Science and Information Technology, Irbid National University, Irbid 21110, Jordan;

[4] The School of Information Technology, Sebha University, Sebha 71, Libya;

关键词: IDS;    machine learning;    DoS;    WSN security;    feature selection;    LEACH;   

DOI  :  10.3390/s21144821

来源: DOAJ

【 摘 要 】

Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

【 授权许可】

Unknown