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Improving the Understanding of Malware using Machine Learning.. Georgia Institute of Technology, Atlanta, GA, USA, (2024)base-search.net (ftgeorgiatech:oai:repository.gatech.edu:1853/73147).Enabling Refinable Cross-Host Attack Investigation with Efficient Data Flow Tagging and Tracking., , , , , , , and . USENIX Security Symposium, page 1705-1722. USENIX Association, (2018)situ: Situational understanding and discovery for cyber attacks., , , , , , and . IEEE VAST, page 307-308. IEEE Computer Society, (2012)To believe or not to believe: Validating explanation fidelity for dynamic malware analysis., , and . CVPR Workshops, page 48-52. Computer Vision Foundation / IEEE, (2019)RAIN: Refinable Attack Investigation with On-demand Inter-Process Information Flow Tracking., , , , , , , and . CCS, page 377-390. ACM, (2017)VulChecker: Graph-based Vulnerability Localization in Source Code., , , , , , , and . USENIX Security Symposium, page 6557-6574. USENIX Association, (2023)NV: Nessus vulnerability visualization for the web., , , , and . VizSEC, page 25-32. ACM, (2012)Identifying Behavior Dispatchers for Malware Analysis., , , , , , and . AsiaCCS, page 759-773. ACM, (2021)DeepReflect: Discovering Malicious Functionality through Binary Reconstruction., , , and . USENIX Security Symposium, page 3469-3486. USENIX Association, (2021)