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Conference Aug 2026

A Comparative Study of Machine Learning-Based Intrusion Detection for IoT Networks: Performance and Explainability Evaluation

The attack surface of contemporary networks has significantly increased due to the rapid proliferation of Internet of Things (IoT) devices, making intrusion detection a critical security requirement. This study presents a comparative evaluation of machine learning based intrusion detection models in Internet of Things...

Elang Prasakti Ghani, Bima Marga Ritna, Akka Rafif Sirajuddin et al. · 0 citations
Open access Sep 2026

A Software-Defined IoT Analytics Platform forMulti-Method Anomaly Detection Using Docker and theELKStack

A software-defined IoT analytics platform to incorporate several anomaly detection techniques through a containerized ELK (Elasticsearch Logstash-Kibana) architecture deployed based on the use of Docker, demonstrating the robustness of anomaly detection performance and preserving cost efficiency, scalability, and low d...

K. Al-Tahat, Hamzeh Aljawawdeh, M. Al-Madi et al. · 0 citations
Open access Aug 2026

Machine learning approaches for intrusion Detection in IOT networks

Internet of Things (IoT) technologies have introduced a new complexity in the network environment and made it larger, leading to the demand for accurate, robust and interpretable Intrusion Detection System (IDS). This study presents a machine-learning framework for multi-class IoT intrusion detection system (IDS) with...

Assistant Lecturer Ahmed Ridha Khudhur · 0 citations
Open access Aug 2026

Adaptive Machine Learning Framework for Real-Time Cyber-Attack Detection and Prevention in IoT Networks

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. · 0 citations
Open access Sep 2026

Enhanced anomaly detection in IoT networks via feature fusion and learning-based echo state networks

A hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity that achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.

P. Palpandi, B. Sakthivel, M. Ponnrajakumari et al. · 0 citations
Open access Sep 2026

Novel Approach for Intrusion Detection in IoT Networks Based on ResNet-ABC-Chatterjee Algorithm

The intrusion detection in the Internet of Things (IoT) network presents a number of difficulties, necessitating the skillful use of network device attributes for precise threat identification. With a multi-phase approach, this research provides a novel intrusion detection method. This technique takes advantage of the...

Mahmood Mohassel Feghhi, Raya Majid Alsharfa, Mohammed Faisal · 0 citations

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