Cyber resilient framework with energy efficient swarm routing and ensemble threat detection in fog assisted wireless sensor networks

The rapid growth of Wireless Sensor Networks (WSNs) and their integration with fog computing have enabled faster data processing and reduced reliance on cloud infrastructures. However, these networks remain constrained by limited energy resources, increased latency under dynamic traffic, and heightened vulnerability to cyberattacks. Traditional routing protocols typically optimize either energy efficiency or security, but rarely address both in a unified and adaptive manner. This work proposes a cyber-resilient, energy-optimized routing framework for fog-enabled WSNs that integrates a modified Ant Colony Optimization (ACO) algorithm with an ensemble-based Intrusion Detection System (IDS). The routing layer employs a multi-objective cost function that jointly considers distance, residual energy, and security risk. To enhance adaptability, CatBoost is deployed at energy-constrained sensor nodes for local energy and density assessment, while XGBoost operates at fog nodes to evaluate global path quality and congestion. The IDS ensemble—comprising Support Vector Machines (SVM), k-Nearest Neighbours (KNN), and Long Short-Term Memory (LSTM) networks—detects Denial-of-Service (DoS), Probe, R2L, and U2R attacks in real time. Importantly, detected threats immediately influence routing decisions, enabling compromised links to be bypassed without disrupting network operations. Extensive MATLAB simulations show that the proposed framework achieves 96.5% energy savings, an 85.83% latency reduction, and an 89% intrusion detection rate, validated through statistical analysis across multiple runs. By transforming IDS from a passive monitoring tool into an active routing controller, this work delivers a secure, adaptive, and energy-efficient solution for dynamic and resource-constrained IoT and WSN environments.