Designing Efficient Sinkhole Attack Detection Mechanism in Edge-Based IoT Deployment.

Area of Science:

• Computer Science
• Network Security
• Internet of Things

Background:

• Sinkhole attacks pose a significant threat to the functionality and reliability of edge-based Internet of Things (EIoT) networks.
• These attacks disrupt communication by attracting normal nodes to malicious paths, leading to data loss, reduced throughput, and increased delay.

Purpose of the Study:

• To propose an effective intrusion detection scheme, SAD-EIoT, to mitigate sinkhole attacks in EIoT environments.
• To enhance the security, efficiency, and reliability of EIoT communications against sophisticated network threats.

Main Methods:

• The SAD-EIoT scheme utilizes resource-rich edge nodes (edge servers) to detect sinkhole attacker nodes.
• Detection is achieved through message exchange between edge nodes to identify malicious activities.
• The scheme's performance was practically demonstrated and evaluated using the NS2 simulator.

Main Results:

• SAD-EIoT achieved a high detection rate of approximately 95.83% and a low false positive rate of 1.03%.
• The proposed scheme demonstrated superior performance compared to existing related schemes in detecting sinkhole attacks.
• SAD-EIoT proved to be efficient in terms of computation and communication costs.

Conclusions:

• SAD-EIoT offers a robust and efficient solution for detecting and mitigating sinkhole attacks in EIoT networks.
• The scheme's high accuracy and low overhead make it suitable for critical applications like surveillance and monitoring systems.
• SAD-EIoT significantly improves network performance parameters degraded by sinkhole attacks.