article · Procedia Computer Science
Wireless sensor networks rely on open-air transmission and operate without fixed infrastructure, leaving them vulnerable to security threats. Intrusion detection systems help identify and block these attacks, but sensor resource constraints create operational challenges. A hybrid, lightweight intrusion detection system addresses this problem by combining a cluster-based architecture with support vector machine anomaly detection and predefined signature rules. The clustered design helps reduce overall energy consumption across the network. By pairing machine learning with rule-based techniques, the framework monitors network activity to uncover malicious behaviours globally. Simulation assessments demonstrate that the approach detects abnormal network events efficiently, achieving a high detection rate alongside a low false alarm frequency.
Wireless sensors are widely deployed for remote monitoring, but their limited power and wireless links make them prime targets for digital attacks. Developing protective systems that accurately spot intrusions without quickly draining battery reserves ensures connected sensors can remain secure, operational, and trustworthy over extended periods without constant maintenance.
This security approach could protect commercial and industrial sensor network deployments where energy efficiency is vital. Network security providers and Internet of Things developers could apply these detection methods within network management software. Because the performance was demonstrated solely through simulations, the technology remains at an early stage of development and requires validation on physical hardware before real-world deployment.
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Many researchers are currently focusing on the security of wireless sensor networks (WSNs). This type of network is associated with vulnerable characteristics such as open-air transmission and self-organizing withoutafixed infrastructure. Intrusion Detection Systems (IDSs) can play an important role in detecting and preventing security attacks. In this paper, we propose a hybrid, lightweight intrusion detection system for sensor networks. Our intrusion detection model takes advantage of cluster-based architecture to reduce energy consumption. This model uses anomaly detection based on support vector machine (SVM) algorithm and aset of signature rules to detect malicious behaviors and provide global lightweight IDS. Simulation results show that the proposed model can detect abnormal events efficiently andhas a high detection rate with lower false alarm.
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DOI: 10.1016/j.procs.2015.05.108
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