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

• URL: http://arxiv.org/abs/2605.13159v1
• Date: Wed, 13 May 2026 08:23:17 GMT
• Title: Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices
• Abstract summary: 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.<n>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.
• Score: 2.6579369175321155
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT [0.4511923587827302]
  We present a lightweight and privacy-preserving botnet detection framework based on federated learning.<n>This approach enables distributed devices to collaboratively train models without exchanging raw data.<n>Experiments on benchmark IoT botnet datasets demonstrate that the framework achieves high detection accuracy while substantially reducing communication costs.
  arXiv  Detail & Related papers  (2025-10-03T20:54:58Z)
• Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning [39.58317527488534]
  This paper proposes a novel method using hybrid statistical-deep learning for the detection, prevention, and source localization of IEC 61850 SV injection attacks.<n>It effectively discards malicious SV frames with minimal processing overhead and latency, maintains robustness against communication network latency variation and time-synchronization issues.<n>Results demonstrate the method's suitability for practical deployment in IEC 61850-compliant digital substations.
  arXiv  Detail & Related papers  (2025-07-01T07:38:22Z)
• Are Trees Really Green? A Detection Approach of IoT Malware Attacks [0.3749861135832073]
  Internet of Things (IoT) devices remain vulnerable due to their resource constraints and difficulty in applying security patches.<n>This paper proposes a green methodology to identify IoT malware networking attacks based on flow privacy-preserving statistical features.
  arXiv  Detail & Related papers  (2025-06-09T15:01:04Z)
• Mutual Information Minimization for Side-Channel Attack Resistance via Optimal Noise Injection [33.634947490938366]
  Side-channel attacks (SCAs) pose a serious threat to system security by extracting secret keys through physical leakages.<n>We propose an optimal artificial noise injection method to minimize the mutual information in systems with Gaussian inputs.
  arXiv  Detail & Related papers  (2025-04-29T08:57:21Z)
• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
  Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks.<n>This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments.<n>Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
  arXiv  Detail & Related papers  (2025-02-14T23:11:51Z)
• Efficient Denial of Service Attack Detection in IoT using Kolmogorov-Arnold Networks [22.036794530902608]
  This paper introduces a novel lightweight approach to DoS attack detection based on Kolmogorov-Arnold Networks (KANs)<n>KAN achieves state-of-the-art detection performance while maintaining minimal resource requirements.<n>Compared to existing solutions, KAN reduces memory requirements by up to 98% while maintaining competitive detection rates.
  arXiv  Detail & Related papers  (2025-02-03T21:19:46Z)
• Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices [38.16309790239142]
  Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices make it challenging to deploy conventional computing-based IDSs. We propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset.
  arXiv  Detail & Related papers  (2024-06-04T20:36:21Z)
• Enhancing IoT Malware Detection through Adaptive Model Parallelism and Resource Optimization [0.6856683556201506]
  This study introduces a novel approach to malware detection tailored for IoT devices. Based on resource availability, ongoing workload, and communication costs, the malware detection task is dynamically allocated either on-device or offloaded to neighboring IoT nodes. Experimental results demonstrate that this proposed technique achieves a significant speedup of 9.8 x compared to on-device inference.
  arXiv  Detail & Related papers  (2024-04-12T20:51:25Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems [41.1371349978643]
  We present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data.
  arXiv  Detail & Related papers  (2022-08-27T14:50:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.