Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity
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- Nasir, Nida & Kansal, Afreen & Alshaltone, Omar & Barneih, Feras & Shanableh, Abdallah & Al-Shabi, Mohammad & Al Shammaa, Ahmed, 2023. "Deep learning detection of types of water-bodies using optical variables and ensembling," LSE Research Online Documents on Economics 118724, London School of Economics and Political Science, LSE Library.
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- Feng Wu & Wanqiang Xu & Chaoran Lin & Yanwei Zhang, 2022. "Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
- Pengyi Liao & Jun Yan & Jean Michel Sellier & Yongxuan Zhang, 2022. "TADA: A Transferable Domain-Adversarial Training for Smart Grid Intrusion Detection Based on Ensemble Divergence Metrics and Spatiotemporal Features," Energies, MDPI, vol. 15(23), pages 1-18, November.
- Ahmed Abdu & Zhengjun Zhai & Redhwan Algabri & Hakim A. Abdo & Kotiba Hamad & Mugahed A. Al-antari, 2022. "Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
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cybersecurity; machine learning; malware detection; intrusion detection system; spam classification;All these keywords.
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