Hybrid intelligence-powered secure clustering with trust-optimized routing for next-generation MANET communication

Area of Science:

• Computer Science
• Network Security
• Artificial Intelligence

Background:

• Mobile Ad Hoc Networks (MANETs) face challenges in secure, energy-efficient communication due to dynamic topologies and intrusions.
• Existing solutions struggle to balance security, energy efficiency, and reliability in resource-constrained MANETs.

Purpose of the Study:

• To propose a Hybrid Intelligence-Powered Secure Clustering and Trust-Optimized Routing (HISCTR) framework for MANETs.
• To enhance security, energy efficiency, and network longevity in MANETs.

Main Methods:

• Implemented Geographical Adaptive Fidelity Clustering (GAFC) for stable clustering.
• Utilized Addax Optimization Algorithm (AOA) for energy-balanced Cluster Head selection.
• Employed Fuzzy Trust Vector Computation (FTVC) and Halfway Escape Optimization (HEO) for reliable routing.
• Integrated Transformer and CNN models for hybrid deep-learning-based intrusion detection.

Main Results:

• Achieved a mean power consumption of 0.8 mJ.
• Reached a throughput of 0.92 Mbps.
• Demonstrated a network life of 5300 rounds with a 99.5% efficiency rate.
• Showcased superior performance in packet delivery ratio and overall efficiency.

Conclusions:

• The HISCTR framework offers a robust and scalable solution for secure and energy-efficient communication in MANETs.
• Hybrid intelligence integration effectively addresses MANET security and performance challenges.
• The proposed model significantly improves network lifespan and reliability.