Network-based Classification of Authentication Attempts using Machine Learning

2019 International Conference on Computing, Networking and Communications (ICNC)(2019)

引用 0|浏览2
暂无评分
摘要
Network security operators are challenged with protecting an increasing number of clients from authentication-based attacks such as password guessing. Host-based defenses help in preventing such attacks but are difficult to manage and monitor at scale. These challenges open the door for network-based defenses. In this work, we introduce AuthML. AuthML performs protocol-agnostic authentication modeling to detect successful and unsuccessful authentication attempts at the network level. Using machine learning (ML), AuthML operates directly on network communication to determine the outcome of authentication attempts in real time. To show AuthML’s efficacy, we validate our approach on multiple deployment scenarios. AuthML achieves an accuracy of 99.9% examining 29,015 new flows in this operational phase, demonstrating that we can achieve similar performance in real time to state-of-the-art techniques without manual protocol analysis.
更多
查看译文
关键词
network-based classification,machine learning,network security operators,authentication-based attacks,password guessing,host-based defenses,network-based defenses,unsuccessful authentication attempts,network level,network communication,operational phase,protocol-agnostic authentication,AuthML
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要