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