Hybrid ECCMGO–MPCOA framework with ASTRA-based trust and intrusion detection for secure and energy-efficient MANET communication

A mobile ad hoc network (MANET) is a self-configurable network connected by wireless links. This type of network is only suitable for provisional communication links as it is infrastructure-less and there is no centralized control. Wireless Sensor Networks (WSNs) and Mobile Ad Hoc Networks (MANETs) are increasingly employed in mission-critical applications due to the flexibility and scalability. However, the open and dynamic nature of these networks makes it highly susceptible to various security threats, including zero-day attacks, and denial-of-service (DoS), leading to degraded network performance. Mobile Ad Hoc Networks (MANETs) are decentralized and highly dynamic environments that face significant challenges in secure communication and routing efficiency due to node mobility and vulnerability to attacks such as blackhole, Sybil, and DoS. To address these challenges, this framework proposes a novel trust-based secure communication framework that integrates adaptive clustering, optimized routing, and intelligent intrusion detection. The model begins with Density-Aware Possibilistic C-Means (DAPCM) for robust and flexible cluster formation, followed by Enhanced Crisscross Moss Growth Optimization (ECCMGO) for optimal Cluster Head (CH) selection based on energy, trust, and link stability. Each node’s trustworthiness is evaluated through a Multi-Attribute Trust Model (MATM) using behavioral parameters such as packet forwarding ratio, delay, and energy level. These trust scores are embedded into the Modified Pine Cone Optimization Algorithm (MPCOA) to establish secure and efficient routing paths by avoiding low-trust or malicious nodes. To ensure proactive threat detection, the system incorporates an Attention-based Spatio-Temporal Relational Architecture (ASTRA), which captures both spatial interactions and temporal behavioral patterns across nodes. ASTRA enables accurate, real-time detection of attacks by learning complex trust dynamics and network anomalies. Simulation outcomes show significant enhancement in energy efficiency, packet delivery ratio, and intrusion detection accuracy, confirming the model’s suitability for real-time, mission-critical MANET applications.