Deep learning techniques to detect cybersecurity attacks: a systematic mapping study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Software
Link
https://link.springer.com/content/pdf/10.1007/s10664-023-10302-1.pdf
Reference177 articles.
1. Ahmed A, Krishnan VVG, Foroutan SA, Touhiduzzaman M, Srivastava A, Wu Y, Hahn A, Sindhu S (2018) Cyber physical security analytics for anomalies in transmission protection systems. In: IEEE Industry Applications Society Annual Meeting, IAS 2018, Portland, OR, USA, September 23-27, 2018, IEEE, pp 1–8. https://doi.org/10.1109/IAS.2018.8544672
2. Al-Haija Q, Sabatto S (2020) An efficient deep-learning-based detection and classification system for cyber-attacks in iot communication networks. Electronics 9(12):2152. https://doi.org/10.3390/electronics9122152
3. Al-Hawawreh M, Moustafa N, Garg S, Hossain MS (2020) Deep learning-enabled threat intelligence scheme in the internet of things networks. IEEE Transactions on Network Science and Engineering pp 1–1. https://doi.org/10.1109/TNSE.2020.3032415
4. Al-Qatf M, Yu L, Al-Habib M, Al-Sabahi K (2018) Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access 6:52843–52856. https://doi.org/10.1109/ACCESS.2018.2869577
5. Alauthman M, Aslam N, Al-kasassbeh M, Khan S, Al-Qerem A, Raymond Choo KK (2020) An efficient reinforcement learning-based botnet detection approach. J Netw Comp Appl 150:102479. https://doi.org/10.1016/j.jnca.2019.102479
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Towards Enhancing Privacy-Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical Study;ACM Transactions on Software Engineering and Methodology;2024-09-12
2. Prediction of Cyber Attacks Utilizing Deep Learning Model using Network/Web Traffic Data;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05
3. Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA Infrastructure;IEEE Access;2024
4. Deep Learning Approaches for Cyber Threat Detection and Mitigation;Proceedings of the 2023 7th International Conference on Advances in Artificial Intelligence;2023-10-13
5. Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques;Analytics;2023-08-11
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3