Jul 2026· International Conference on Computer, Information and Telecommunication Systems· pp. 1-8· 0 citations· 21 references
Abstract
The rapid growth of Internet of Things (IoT) networks has increased their exposure to cyber threats, while existing Intrusion Detection Systems (IDS) remain largely reactive and resource-intensive. This paper proposes a HMCTI Framework for proactive cyberattack detection in IoT environments. The framework distributes threat intelligence across Edge, Fog, and Cloud layers, integrating behavioral drift analysis, flow-level features, and device-context information to identify attacks at their early stages. By combining lightweight anomaly detection, context-aware threat analysis, and multi-layer threat correlation, HMCTI enhances detection capability while maintaining scalability and efficiency. Experimental evaluation using the CICIoT2023 dataset assesses detection accuracy, Detection Lead Time (DLT), and resource overhead. The proposed framework provides a scalable and proactive approach for early cyberattack detection in next-generation IoT networks.
: The hierarchical and heterogeneous nature of Internet of Things (IoT) architectures, spanning edge, fog, and cloud layers, makes intrusion detection particularly challenging, as each layer provides only a partial view of the system. Traditional intrusion detection systems (IDS) often fail to identify coordinated and...
Myria Bouhaddi, Farah Sadok· International Conference on...· 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
A role-based multi-agent cybersecurity framework for 6G-enabled IoT networks that enables proactive intrusion prediction, real-time detection, and knowledge-driven threat mitigation and a promising step toward scalable and intelligent cybersecurity management in 6G-enabled IoT networks is proposed.
Alaeddine Diaf, A. A. Korba, W. Jaafar et al.· IEEE Open Journal of the Com...· 0 citations
This study proposes an AI driven vulnerability management framework that integrates anomaly detection techniques using machine learning to enhance proactive threat identification and mitigation in IoT ecosystems, and demonstrates that AI based models significantly outperform traditional methods in detecting previously...
Daniel Nafisatu Mshelbila· International Journal of Com...· 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 rapid growth of interconnected digital infrastructures, cloud computing environments, Internet of Things devices, and enterprise networking systems has significantly increased the frequency, complexity, and sophistication of cyberattacks targeting organizational information assets. Traditional cybersecurity mechani...
S. Tamilselvi· Journal of Intelligent Decis...· 0 citations
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