Applying Artificial Intelligence Methods to Network Attack Detection
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-319-98842-9_5
Reference40 articles.
1. Branitskiy A, Kotenko I (2016) Analysis and classification of methods for network attack detection. In: SPIIRAS Proceedings, vol 45(2), pp 207–244. https://doi.org/10.15622/sp.45.13 (in Russian)
2. Branitskiy A, Kotenko I (2015) Network attack detection based on combination of neural, immune and neuro-fuzzy classifiers. In: Plessl C, Baz DE, Cong G, Cardoso JMP, Veiga L, Rauber T (eds) Proceedings of the 18th IEEE International Conference on Computational Science and Engineering, IEEE Computer Society, Los Alamitos, CA, USA, pp 152–159. https://doi.org/10.1109/CSE.2015.26
3. Branitskiy A, Kotenko I (2017) Hybridization of computational intelligence methods for attack detection in computer networks. J Comput Sci 23:145–156. https://doi.org/10.1016/j.jocs.2016.07.010
4. Branitskiy A, Kotenko I (2017) Network anomaly detection based on an ensemble of adaptive binary classifiers. Computer Network Security. In: Rak J, Bay J, Kotenko I, Popyack L, Skormin V, Szczypiorski K (eds) Computer network security, pp 143–157. Springer, Cham. https://doi.org/10.1007/978-3-319-65127-9_12
5. Abraham A, Thomas J (2006) Distributed intrusion detection systems: a computational intelligence approach. In: Abbass HA, Essam D (eds) Applications of information systems to homeland security and defense. Idea Group, Hershey, PA, USA, pp 107–137. https://doi.org/10.4018/978-1-59140-640-2.ch005
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. Anomaly Detection in Cybersecurity Datasets via Cooperative Co-evolution-based Feature Selection;ACM Transactions on Management Information Systems;2022-02-04
2. Fake Post Detection Using Graph Neural Networks;Automatic Control and Computer Sciences;2021-12
3. Zum Stand der Dinge;Security by Design;2021
4. Modification of Recursive Kalman Filter Algorithm for Adaptive Prediction of Cyber Resilience for Industrial Systems;Nonlinear Phenomena in Complex Systems;2020-10-28
5. Machine Learning and Big Data Processing for Cybersecurity Data Analysis;Data Science in Cybersecurity and Cyberthreat Intelligence;2020
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