Integrated Visual Analytics Approach against Multivariate Cybersecurity Attack

Abstract

As security threats rapidly spread all over the world, it is critical that network traffic is monitored and protected from abnormal attacks during 24/7. Even though various security devices (Rep., network intrusion detection system, NIDS) had utilized to guarantee a solid network security, it still depends on human being due to complex patterns from unknown threats. This study introduces a graphical interactive system for representing and understanding multivariate cybersecurity attacks. In particular, the interface enhances intuitive judgments combined with machine learning-based analysis of suspicious traffic