Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity
Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity
Blake Anderson,D. McGrew
2017 · DOI: 10.1145/3097983.3098163
Knowledge Discovery and Data Mining · 引用 257 次
TLDR
This paper designs and carries out experiments that show how six common algorithms perform when confronted with real network data, and identifies the situations in which certain classes of algorithms underperform on the task of encrypted malware traffic classification.