12 citations · 13 across the 4 of their papers we have counts for
5 papers
Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification
Igor Cherepanov, David Sessler, Alex Ulmer +2
Machine learning (ML) has become the dominant approach for network traffic classification, achieving very high predictive performance. However, a model is only valuable if it learn…
Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data
Igor Cherepanov, David Sessler, Alex Ulmer +3
Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of comput…
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features
Igor Cherepanov, David Sessler, Alex Ulmer +2
Deep learning (DL) models achieve remarkable performance in classification tasks. However, models with high complexity can not be used in many risk-sensitive applications unless a…
Extension of Dictionary-Based Compression Algorithms for the Quantitative Visualization of Patterns from Log Files
Igor Cherepanov, Jonathan Geraldi Joewono, Arjan Kuijper +1
Many services today massively and continuously produce log files of different and varying formats. These logs are important since they contain information about the application act…
Visualization Of Class Activation Maps To Explain AI Classification Of Network Packet Captures
Igor Cherepanov, Alex Ulmer, Jonathan Geraldi Joewono +1
The classification of internet traffic has become increasingly important due to the rapid growth of today's networks and applications. The number of connections and the addition of…