A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks.

Area of Science:

• Computer Science
• Network Security
• Wireless Sensor Networks

Background:

• Cluster-based wireless sensor networks (WSNs) rely on cluster heads (CHs) for data aggregation and forwarding to the sink node (SN).
• This architecture, while efficient for energy balancing, is susceptible to selective forwarding attacks launched by compromised CHs that drop packets.

Purpose of the Study:

• To propose a novel data clustering algorithm (DCA-SF) for the effective detection and isolation of malicious CHs in WSNs.
• To enhance the robustness of the detection algorithm through adaptive parameter tuning.

Main Methods:

• A data clustering algorithm (DCA) is adapted to identify malicious CHs based on their cumulative forwarding rates (CFRs).
• The DCA parameters (Eps, Minpts) are adjusted adaptively to improve detection accuracy.
• Simulations are conducted to evaluate the performance of the proposed DCA-SF algorithm.

Main Results:

• The DCA-SF algorithm demonstrates a low missed detection rate of 1.04%.
• A low false detection rate of 0.42% was achieved.
• The algorithm operates with low energy consumption.

Conclusions:

• The proposed DCA-SF algorithm effectively detects and isolates malicious cluster heads in WSNs.
• Adaptive parameter tuning enhances the algorithm's reliability and efficiency.
• DCA-SF offers a promising solution for securing WSNs against selective forwarding attacks with minimal energy overhead.