The rapid growth of Internet of Things (IoT) technologies has enabled transformative applications in smart cities, environmental monitoring, and sustainable infrastructure. However, the pervasive deployment of IoT devices exposes these ecosystems to significant security and privacy risks, thereby necessitating robust intrusion detection systems (IDS). Conventional IDS approaches often impose high computational and energy demands, which limit their applicability in resource-constrained and environmentally sensitive settings. To address these challenges, this study proposes a privacy-preserving and energy-efficient intrusion detection framework designed specifically for IoT-enabled environments. The framework integrates lightweight cryptographic mechanisms with optimized feature selection and machine learning-based detection to safeguard user data while minimizing resource utilization. Experimental evaluation demonstrates that the proposed model achieves superior detection accuracy, reduced false alarm rates, and up to 25% lower energy consumption compared to conventional IDS approaches. These results highlight the potential of the framework to enhance security resilience in IoT ecosystems while contributing to environmentally sustainable computing practices.
Swetha et al. (Mon,) studied this question.