LAVA-Control: Stable Cost-Aware Intelligence Density Adaptation for Distributed IDS in Heterogeneous IoT Networks

Abstract

In ubiquitous networks, sensing and compute resources are distributed across numerous heterogeneous nodes. Consequently, fixed Intrusion Detection Systems (IDS) deployments either lead to resource wastage or leave security gaps. LAVA-Control addresses this challenge by treating intelligence density, which is the number and identity of nodes executing detection tasks-as a first-class control variable. Operating atop a trained machine learning detector, it employs policy-driven bounds, particle swarm optimization (PSO)-based subset selection, and cooldown timers to adaptively select node subsets, while maintaining constraints on latency, energy consumption, and residual risk. To ensure system robustness, LAVA-Control introduces explicit stability and oscillation metrics that quantify the smoothness of system responses under varying traffic and attack scenarios. Empirical evaluations on vehicular and Distributed Denial-of-Service (DDoS) datasets demonstrate that LAVA-Control achieves near-baseline detection accuracy at a reduced operational cost compared to heuristic baselines, and exhibits substantially greater stability in its adaptations than reinforcement-learning-based control mechanisms.