Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices

2026-05-13

AI Summary

This work addresses the challenge of securing resource-constrained Internet of Things (IoT) devices against prevalent threats such as denial-of-service and man-in-the-middle attacks. To this end, the authors propose a lightweight intrusion detection approach tailored for microcontrollers, which integrates an optimized decision tree with a compact neural network to achieve high-accuracy, real-time detection under stringent memory and computational constraints. Experimental evaluation in heterogeneous IoT environments demonstrates that the proposed method attains detection accuracies of 99% using the decision tree component and 96% with the neural network component, substantially outperforming existing solutions. The approach effectively balances security assurance with deployment efficiency and hardware limitations, offering a practical defense mechanism for low-resource IoT deployments.

Abstract

IoT devices particularly microcontrollers are challenged by their inherent limitations in processing capabilities, memory capacity, and energy conservation. Securing communication within IoT networks is further complicated by the heterogeneity of devices and the myriad of potential security threats. Our study introduces a lightweight model that utilises machine learning algorithms to achieve a notable detection accuracy of 99% using a decision tree method and 96% using a neural network in identifying cyber threats, including Denial of Service and Man-in-the-Middle attacks which make up the majority of the attacks these devices face. While the decision tree method offers higher accuracy, it requires more computational resources, whereas the neural network approach, despite a slightly lower accuracy, is more memory-efficient. Both methods enhance the real-time monitoring and defence of IoT networks, safeguarding the transmission of data. Additionally, our approach is tailored to conserve memory and optimise computational demands, rendering it suitable for deployment on microcontrollers with limited resources.

Problem

• IoT security
• intrusion detection
• resource-constrained devices
• microcontrollers
• cyber threats

Innovation

• on-device intrusion detection
• lightweight machine learning
• resource-constrained IoT
• decision tree
• neural network