A hybrid explainable machine learning and deep learning framework for IoT network anomaly detection
· 1 citation
· 27 references
Similar papers
Open access
Sep 2026
A Hybrid Deep Learning-Based Intrusion Detection System for IOT Devices
The rapid growth of the Internet of Things (IoT) has created a highly interconnected digital environment, but this expansion has also introduced complex security risks, especially at the application layer where most user-level operations occur. Many existing intrusion detection systems (IDS) are unable to cope with...
F. C. Uzoezie
· International Journal of Com... · 0 citations
Open access
Sep 2026
Hybrid Deep Learning Behavioral Detection of MITM, Spoofing, and Flooding Attacks in IoT Networks.
Current IoT intrusion detection research has shown very high performance on crafted datasets, but its practical usefulness is constrained by three unresolved data issues: highly skewed class distribution, overlapping profiles of similar attack patterns, and lack of convincing evidence that synthetic sample augmentation...
A. Hammad, K. Oza
· Journal of Kufa for Mathemat... · 0 citations
Open access
Sep 2026
Machine Learning-Based Anomaly Detection for Traffic in IoT-Enabled Transportation Networks
This study investigates the effectiveness of maching learning approaches for supervised malicious traffic classification in IoT networks using the ACI-IoT-2023 dataset and shows strong classification performance across the evaluated approaches.
Connor Gladish, Molly Corgan, J. Moss et al.
· Electronics · 0 citations
Open access
Oct 2026
Explainable Machine Learning for Real-Time Cyber Threat Detection: An Intelligent Framework for Adaptive Information Security
The cyber threats in IoT environments are constantly changing and need accurate, interpretable, efficient and adaptive intrusion detection. This research suggests an explainable machine-learning framework with the CICIoT2023 dataset. Logistic Regression, Decision Tree, Random Forest, XGBoost, and Explainable Boosting M...
S. Gusai, Shrikant Telang, R. Balakrishnan et al.
· Computer and Decision Making... · 0 citations
Open access
Oct 2026
An IoT Malware Detection Framework Based on Large Language Models and Deep Learning Techniques
Rapid growth in IoT devices has led to the creation of crucial cybersecurity threats since existing methods of malware detection which use signature matching and numerical feature analysis are incapable of capturing the contextual behavioral semantics of IoT attacks. The presented work proposes an innovative malware de...
Hamza K. Al-Daoud, A. al-Helali
· Symmetry · 0 citations
Open access
Sep 2026
A Deep Learning-Based Framework for Detecting and Classifying Malicious Network Activities in Complex Cybersecurity Environments
The rapid growth of interconnected digital networks has increased the need for reliable and intelligent approaches to detect and classify malicious network activities in complex cybersecurity environments. This study developed a deep learning-based framework for detecting and classifying malicious network activities us...
Mashal Fatima
· Journal of Global Social Tra... · 0 citations