Jul 2026· International Conference on Future Internet of Things and Cloud· pp. 27-34· 0 citations· 15 references
Abstract
Traditional Internet of Things (IoT) security solutions often rely on heavy cloud-based or gateway-class infrastructure, which is frequently unsuitable for resource-limited hardware due to latency, power, and memory constraints. This paper proposed a resource-aware behavioral Intrusion Detection System (IDS) designed for highly constrained IoT devices. To address these challenges, the proposed system implements real-time application-layer monitoring on an ESP32 Microcontroller Unit (MCU) and utilizes an offline-trained logistic regression model for autonomous, on-device inference. The detection mechanism extracts behavioral features, such as request rates, failed authentication attempts, and invalid resource access within sliding time windows to estimate attack probabilities. Experimental evaluations under controlled scenarios involving flood, brute force, and scan attacks demonstrate that the system achieves high accuracy, precision, and recall. These findings indicate that effective cyber intrusion detection and local mitigation can be successfully executed directly on a single MCU while preserving stable runtime performance and minimal memory usage. Finally, this paper highlights the need for further optimizations to improve robustness and scalability.
A hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov–Smirnov (KS)-based concept drift monitoring is presented, providing initial evidence of generalization to one held-out attack family but should not be interpreted as proof of...
Muath A. Obaidat, Meryem Abouali, Aneeza Shakeel· Italian National Conference...· 0 citations
This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks that integrates Isolation Forest, Autoencoder, Extreme Gradient Boosting, and Bidirectional Long Short-Term Memory models within a stacked ensemble architecture to improve attack detection while re...
Ruthwik Palem, Likhith Reddy Peketi, Vanathi M et al.· Cureus Journal of Computer S...· 0 citations
This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.
P. P. Bairagi, Ashish Bagwari, Sailen Dutta Kalita et al.· international journal of eng...· 0 citations
The results verify the framework's ability to provide low latency and correct DDoS mitigation directly on the IoT devices, which can be considered a feasible solution to achieve resilience improvement of critical IoT deployments in health care, industrial automation, and smart cities.
Selvi T, Jayaganesh J· International journal of com...· 0 citations
An AI-driven framework for intrusion detection and energy-efficient routing in IoT networks is proposed that uses machine-learning techniques to identify malicious network traffic and classify it as normal or abnormal and applies an energy-aware routing strategy to select communication paths.
M. Mamoun, Yaqoub Mmoun, Mr. Abdulghader Jalgum· 0 citations
Internet of Things (IoT) device security remains a concern due to their limited computational resources and increasing exposure to network-based cyberattacks. While recent IoT security research has focused on machine-learning and blockchain-based defense mechanisms, many of these approaches introduce computational over...
Kuberan Dharmalingam, Sumendra Yogarayan, Ang Ee Mae· International Conference on...· 0 citations
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