Abstract
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.
Highlights
Wireless network technology is at the heart of the growth of the Internet of Things (IoT)
With regard to the classifier algorithms used with the Water Cycle (WC) feature selection technique, we found that the Decision Tree (DT) and DL algorithms were the best in terms of performance accuracy and number of identified Wireless Sensor Networks (WSNs)-DS features
We modified the Low Energy Aware Cluster Hierarchy (LEACH) clustering protocol to improve its performance by adding various factors such as WSN node residual power, distance between WSN nodes, and the distance between the candidate CHs and the Access Point (AP)
Summary
Wireless network technology is at the heart of the growth of the Internet of Things (IoT). Since perception devices depend on public wireless networks, the perception layer is considered one of the most sensitive topics in need of attention, to protection against attack [3]. Accreditation within this layer contributes to many issues in WSN architecture. Attackers can listen in on radio transmissions, send fake messages over communication channels, and alter received data packets [5,6] They can use compromised WSN nodes with similar hardware resources to legitimate network nodes [7]. The CH selection process in LEACH takes place in two phases in each loop: CH establishment and stead4yo-fs2ta5te [38]
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