SecGrid: a Visual System for the Analysis and ML-based Classification of Cyberattack Traffic
Muriel Figueredo Franco, Jan von der Assen, Luc Boillat, Christian Killer, Bruno Rodrigues, Eder J. Scheid, Lisandro Zambenedetti Granville, Burkhard Stiller · 2021
Due to the increasing number of cyberattacks and respective predictions for the upcoming years with even larger numbers of occurrences, companies are becoming aware not only that the digitization of their businesses is essential, but also that the adoption of efficient cybersecurity strategies is crucial. Therefore, approaches for a better understanding and analysis of cybersecurity are essential.Thus, SecGrid, a Machine Learning (ML) empowered platform for analyzing, classification, and visualization of cyberattacks is introduced. SecGrid implements an extensible set of miners to analyze information from network traces to provide insightful visualizations of malicious traffic given and to classify automatically different types of cyberattacks by using supervised ML. Experiments conducted show high overall usability, scalability in terms of the capacity of the platform to extract information from large files, and high performance and accuracy during the classification of cyberattacks.