Extensible machine learning for encrypted network traffic application labeling via uncertainty quantification
S Jorgensen, J Holodnak, J Dempsey… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
With the increasing prevalence of encrypted network traffic, cybersecurity analysts have
been turning to machine learning (ML) techniques to elucidate the traffic on their networks …
been turning to machine learning (ML) techniques to elucidate the traffic on their networks …
GRAIN: Granular multi-label encrypted traffic classification using classifier chain
Granular traffic classification categorizes traffic into detailed classes like application names
and services. Application names represent parent applications, such as Facebook, while …
and services. Application names represent parent applications, such as Facebook, while …
[HTML][HTML] Improved temporal IoT device identification using robust statistical features
Abstract The Internet of Things (IoT) is becoming more prevalent in our daily lives. A recent
industry report projected the global IoT market to be worth more than USD 4 trillion by 2032 …
industry report projected the global IoT market to be worth more than USD 4 trillion by 2032 …
Granular network traffic classification for streaming traffic using incremental learning and classifier Chain
In modern networks, network visibility is of utmost importance to network operators.
Accordingly, granular network traffic classification quickly rises as an essential technology …
Accordingly, granular network traffic classification quickly rises as an essential technology …