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The main contributions of this work include an evaluation using traffic data from real IoT equipment, and the assessment of traditional machine learning methods ...
Evaluation of AI-based Malware Detection in IoT Network Traffic. DOI: 10.5220/0011279600003283. In Proceedings of the 19th International Conference on ...
In the last few years, some works proposed using Machine Learning (ML) for intrusion and anomaly detection and network vulnerability analysis on the ...
In this work, we use deep and traditional machine learning to identify anomalous traffic in the IoT-23 dataset, which contains network traffic from real-world ...
Evaluation of AI-based Malware Detection in IoT Network Traffic. Topics: Intrusion Detection & Prevention; Machine Learning and AI Security.
This paper reviews literature on deep learning techniques that are used for malware detection. The deep learning methods used for malware detection include CNN ...
Notably, the research demonstrates that despite the reduction in data volume, the malware detection performance remains consistently robust. [18] The widespread ...
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Nov 20, 2023 · The goal of this thesis project is to provide better security for these devices with the help of machine learning algorithms and reverse ...
Missing: Traffic. | Show results with:Traffic.
Aug 1, 2024 · This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks.
Mar 8, 2023 · This work proposes a new systematic approach to identifying modern malware using dynamic deep learning-based methods combined with heuristic approaches.
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