Wireless networks like MANETs, WSNs, and IoT are widely used in critical applications but face serious security threats. Among these, the wormhole attack is highly dangerous as it creates a fake shortcut path, disrupting routing and communication. Traditional detection methods struggle because wormholes are stealthy and hard to identify. Machine Learning (ML) offers a powerful solution by analyzing traffic patterns and detecting anomalies. ML-based wormhole detection improves accuracy, energy efficiency, and overall network security.
Background of Wormhole Attack
A wormhole attack occurs when two malicious nodes create a tunnel to forward packets, making distant nodes appear closer. This misleads routing protocols into selecting false paths controlled by attackers. The attack enables eavesdropping, packet drops, and disruption of network services. In MANETs, WSNs, and IoT, it breaks route discovery, drains energy, and threatens data integrity. ML-based Wormhole Attack Detection helps identify anomalies and secure communication effectively.
Machine Learning for Attack Detection
Machine Learning provides strong solutions for detecting wormhole attacks in wireless networks. Supervised methods like SVM and Random Forest classify normal and malicious routing behaviors. Unsupervised techniques detect anomalies without requiring labeled data. Deep learning models capture complex traffic patterns, reducing false positives. ML-based detection ensures secure and energy-efficient communication.
FAQs on ML-Based Wormhole Attack Detection
Q1: Why is wormhole attack dangerous? It allows malicious nodes to create a shortcut tunnel, disrupting routing paths and compromising network security.
Q2: Can ML detect wormhole attack in MANET? Yes. ML models such as Random Forest, SVM, and deep learning identify abnormal routing patterns.
Q3: Which simulation tools are used? ns-3, OMNeT++, and Cooja Contiki are widely used for simulating wormhole attacks and testing ML-based detection models.
Q4: What metrics are used? Accuracy, detection rate, false positives, energy consumption, and delay are key metrics for evaluating ML-based wormhole detection.
Q5: Can ML-based detection be applied in IoT? Yes. Lightweight ML models can be optimized for IoT devices to ensure secure and energy-efficient communication.
What is a Wormhole Attack?
A wormhole attack steals information from wireless networks by creating malicious tunnels. Since most security data is transmitted over wireless technologies, this attack poses a major threat today.
Behaviour of Wormhole Attack
The wormhole attack disrupts the network in ways that are difficult to trace. Attackers can record transmitted information by placing harmful nodes between legitimate ones using a tunnel. All nodes in the tunnel are controlled by the attacker.
ML-Based Wormhole Attack Detection in WSN
ML-Based Wormhole Attack Detection in WSN improves routing security by identifying malicious tunnels and anomalies in network traffic. An Intrusion Detection System detects wormhole attacks by identifying abnormal network behavior. If any node appears out of range, there is a high chance of attack.
Two modules are used: centralized and distributed. In the centralized module, distance between nodes is validated against transmission range, and duplicate packets are discarded. In the distributed module, neighbor information is shared, and packets are forwarded through other nodes. Various algorithms ensure effective detection and mitigation. Experiments measure detection rates, energy use, and packet overhead.
Experiments
ML-Based Wormhole Attack Detection in WSN improves routing security by identifying malicious tunnels and anomalies in network traffic. Experiments measure detection rates, energy use, and packet overhead. Centralized modules often yield more effective results, making them suitable for large-scale protection of wireless networks.
