Complexity-Aware Deep Learning Framework for Intrusion Detection in Resource-Constrained Networks

Artificial intelligence (AI) has emerged as a powerful tool for securing modern networks against increasinglysophisticated cyberattacks. However, many existing AI-basedintrusion detection systems (IDS) prioritise detection accuracywhile neglecting computational complexity, thereby limiting theirapplicability in resource-constrained environments such as Internet of Things (IoT) devices, edge computing nodes, and embeddedsystems. This paper presents a complexity-aware deep learningframework that explicitly integrates algorithmic complexity considerations into the design, feature selection, and learning stagesof IDS models. Rather than introducing a new deep architecture,the framework achieves a favourable accuracy–efficiency tradeoff by coupling gradient-based dynamic feature pruning witha lightweight neural network, supported by theoretical timeand space complexity analysis. Experiments on two benchmarkdatasets (NSL-KDD and CICIDS2017) demonstrate that theproposed method matches or surpasses the accuracy of stateof-the-art models while significantly reducing FLOPs, parameters, and inference cost. The study bridges the gap betweeninformation security, artificial intelligence, and computationalcomplexity theory, offering a simple, theoretically grounded,and reproducible pathway for deploying secure AI systems inresource-constrained environments.