DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks

Area of Science:

• Computer Science
• Network Security
• Wireless Communication

Background:

• Underwater Wireless Sensor Networks (UWSNs) face challenges in reliable data transmission due to harsh channel conditions.
• Opportunistic Routing (OR) protocols are used for efficiency but are vulnerable to routing attacks like sinkhole attacks.
• Malicious nodes can disrupt data flow by manipulating routing information.

Purpose of the Study:

• To propose a novel intrusion detection scheme (IDS) for OR in UWSNs to mitigate the impact of malicious nodes.
• To enhance the security and reliability of data transmission in large-scale UWSNs.
• To develop a small-sample IDS suitable for sparse node deployment in UWSNs.

Main Methods:

• Proposed DOIDS (Density-Based Spatial Clustering of Applications with Noise for OR) utilizes a local monitoring mechanism.
• Feature selection includes energy consumption, forwarding behavior, and link quality of candidate nodes.
• DBSCAN clustering algorithm is employed to detect abnormal nodes, with a decision function using a time decay function to reduce false detection rates.

Main Results:

• DOIDS effectively improves detection accuracy rates by 3% to 15% across different scenarios.
• The proposed scheme significantly reduces the false positive rate in intrusion detection.
• Simulation results validate the effectiveness of DOIDS in enhancing network security.

Conclusions:

• DOIDS offers a robust solution for detecting malicious nodes in UWSNs employing opportunistic routing.
• The scheme enhances overall network reliability and security by accurately identifying and mitigating threats.
• DOIDS is particularly suitable for sparse UWSN deployments due to its small-sample IDS approach.