A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks.

Area of Science:

• Computer Science
• Network Security
• Wireless Sensor Networks

Background:

• Insider packet drop attacks pose a significant threat to network integrity.
• Existing trust mechanisms struggle to differentiate between attack-induced and normal packet drops.
• Current models lack the speed and accuracy needed for effective threat mitigation.

Purpose of the Study:

• To develop a novel trust model for detecting insider packet drop attackers in wireless sensor networks.
• To enhance detection speed and accuracy by analyzing consecutive packet drops.
• To integrate a new trust model with existing mechanisms for improved overall network performance.

Main Methods:

• Proposed a new trust model based on the observation that insider attacks cause more consecutive packet drops than network failures.
• Developed a hybrid trust mechanism combining the new model with existing trust mechanisms.
• Conducted extensive simulations using OPNET (Optimized Network Engineering Tool) with a geographic greedy routing protocol.

Main Results:

• The proposed hybrid trust model demonstrated superior performance compared to existing models in detecting all types of insider packet drop attacks.
• Achieved significant improvements in detection speed and accuracy.
• Enhanced other critical network performance metrics, including packet delivery rate, routing reliability, and energy efficiency.

Conclusions:

• The novel trust model effectively utilizes consecutive packet drops for faster and more accurate detection of insider threats.
• The hybrid approach offers a robust solution for securing wireless sensor networks against packet drop attacks.
• The findings highlight the potential for improved network security and performance through advanced trust mechanisms.