Hybrid Multi-Level Detection and Mitigation of Clone Attacks in Mobile Wireless Sensor Network (MWSN)

Area of Science:

• Computer Science
• Network Security
• Cybersecurity

Background:

• Wireless sensor networks (WSNs) are vulnerable to node replication attacks in hostile environments.
• Node replication attacks, where adversaries create duplicate nodes, compromise network integrity and security.
• The mobility of nodes in mobile wireless sensor networks (MWSNs) exacerbates the challenge of detecting these attacks.

Purpose of the Study:

• To propose an extended multi-level replica detection technique based on Danger Theory (DT).
• To develop a hybrid approach (centralized and distributed) for enhanced security in MWSNs against clone attacks.
• To evaluate the performance of the proposed DT method against existing security approaches.

Main Methods:

• Implementation of a multi-level detection strategy rooted in Danger Theory (DT).
• Utilizing a hybrid architecture combining centralized and distributed detection mechanisms.
• Assessing performance through key security parameters: false negative rate, energy consumption, detection time, communication overhead, and detection delay.

Main Results:

• The proposed hybrid DT method successfully detects and mitigates malicious replica activities in MWSNs.
• Simulation results indicate superior performance compared to previous centralized and distributed approaches.
• The approach enhances MWSN performance by reducing communication and memory overhead.

Conclusions:

• The extended multi-level Danger Theory approach provides a robust defense against node replication attacks in MWSNs.
• The hybrid nature of the technique offers improved security and efficiency over traditional methods.
• Continuous monitoring and multi-level detection are crucial for securing WSN communications against evolving threats.