RPLAD3: anomaly detection of blackhole, grayhole, and selective forwarding attacks in wireless sensor network-based...

Area of Science:

• Computer Science
• Network Security
• Wireless Sensor Networks

Background:

• Routing Protocol for Low Power and Lossy Networks (RPL) is crucial for Wireless Sensor Networks (WSN) and the Internet of Things (IoT).
• RPL lacks robust defenses against internal attacks like blackhole and grayhole, despite using cryptographic security.
• Existing research on RPL internal attack detection has not adequately addressed mobility frameworks vital for IoT.

Purpose of the Study:

• To present a novel, lightweight system for detecting grayhole, blackhole, and selective forwarding attacks in RPL.
• To integrate a trust model into RPL for enhanced attack detection, specifically considering mobility.
• To introduce the RPLAD3 system, designed for immediate operation post-network initialization.

Main Methods:

• Development of a four-layer system named RPLAD3 for anomaly detection.
• Implementation of a trust model within the RPL protocol to identify malicious nodes.
• Evaluation of RPLAD3's effectiveness under dynamic mobility conditions.

Main Results:

• RPLAD3 demonstrated superior performance compared to the standard RPL protocol in mitigating internal attacks.
• The system achieved high accuracy and a high true positive ratio in attack detection.
• RPLAD3 significantly improved the packet delivery ratio and reduced the false positive ratio to zero, while lowering power consumption.

Conclusions:

• The proposed RPLAD3 system effectively strengthens RPL against critical internal attacks in WSNs.
• The trust model and lightweight design make RPLAD3 suitable for resource-constrained IoT environments with mobility.
• RPLAD3 offers a promising solution for securing WSNs against sophisticated internal threats.