Machine Learning Techniques for Anomaly Detection in Network Traffic
Author:
Affiliation:
1. Amity University,Amity Institute of Information Technology,Lucknow,India
2. Sreyas Institute of Engineering and Technology,Computer Science & Engineering,Hyderabad,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9702520/9702537/09702647.pdf?arnumber=9702647
Reference23 articles.
1. Decision Tree with Sensitive Pruning in Network-based Intrusion Detection System
2. An intrusion detection scheme based on the ensemble of discriminant classifiers
3. Optimizing a New Intrusion Detection System Using Ensemble Methods and Deep Neural Network
4. HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning
5. Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector
Cited by 13 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. A novel graph convolutional networks model for an intelligent network traffic analysis and classification;International Journal of Information Technology;2024-08-14
2. Anomaly detection framework for highly scattered and dynamic data on large-scale networks using AWS;International Journal of Information Technology;2024-04-25
3. Automated Multi-Page Document Classification and Information Extraction for Insurance Applications using Deep Learning Techniques;2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO);2024-03-14
4. Anomaly Detectionin Network Traffic Scenarios by Resampling and Majority Voting with Concept Drift: A Hybrid Approach;2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO);2024-03-14
5. In-Depth Network Traffic Analysis using Machine Learning Perspective: Characterization and Classification;2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI);2023-12-11
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