Abstract
In this paper, we present a method of handling the visualization of hetereogeneous event traffic that is generated by intrusion detection sensors, log files and other event sources on a computer network from the point of view of detecting multistage attack paths that are of importance. We perform aggregation and correlation of these events based on their semantic content to generate Attack Tracks that are displayed to the analyst in real-time. Our tool, called the Event Correlation for Cyber-Attack Recognition System (EC-CARS) enables the analyst to distinguish and separate an evolving multistage attack from the thousands of events generated on a network. We focus here on presenting the environment and framework for multistage attack detection using ECCARS along with screenshots that demonstrate its capabilities.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
