Efficient Machine Learning-Based Security Monitoring and Cyberattack Classification of Encrypted Network Traffic in Industrial Control Systems

F Specht, J Otto - 2024 IEEE 29th International Conference on …, 2024 - ieeexplore.ieee.org
Security monitoring is a key aspect to detect cyberattacks against industrial control systems.
However, with the increasing use of encryption in industrial communication protocols …

CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting

J Koumar, K Hynek, T Čejka, P Šiška - arxiv preprint arxiv:2409.18874, 2024 - arxiv.org
Anomaly detection in network traffic is crucial for maintaining the security of computer
networks and identifying malicious activities. One of the primary approaches to anomaly …

Analysis of statistical distribution changes of input features in network traffic classification domain

L Jančička, J Koumar, D Soukup… - NOMS 2024-2024 IEEE …, 2024 - ieeexplore.ieee.org
This study investigates the evolving landscape of network traffic monitoring, which is crucial
for maintaining computer network services and security. Traditional methods like Deep …

MFWDD: Model-based Feature Weight Drift Detection Showcased on TLS and QUIC Traffic

L Jančička, D Soukup, J Koumar… - … on Network and …, 2024 - ieeexplore.ieee.org
Machine learning (ML) represents an efficient and popular approach for network traffic
classification. However, network traffic inspection is a challenging domain and trained …

Research on Lightweight Network Traffic Service Classification with Interference Traffic in Large-Scale Networks

B Zhang, W Jiang, Q Zhu, C Liao, W Wang - Available at SSRN 5127367 - papers.ssrn.com
This study puts forward a lightweight framework for traffic classification with interferenced
traffic (LF-ITC) to mitigate the high deployment costs of traffic classification facilities in large …