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Bridging the Gap: A Study of AI-based Vulnerability Management between Industry and Academia.

, , , , , , and . DSN-S, page 80-87. IEEE, (2024)

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eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys, and . (2017)cite arxiv:1702.08568.Visualization of shared system call sequence relationships in large malware corpora., , and . VizSEC, page 33-40. ACM, (2012)CrowdSource: Automated inference of high level malware functionality from low-level symbols using a crowd trained machine learning model., , and . MALWARE, page 68-75. IEEE Computer Society, (2014)CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models., , , , , , , , , and 3 other author(s). CoRR, (2024)Malicious Behavior Detection using Windows Audit Logs., , and . AISec@CCS, page 35-44. ACM, (2015)SEEM: a scalable visualization for comparing multiple large sets of attributes for malware analysis., , , , and . VizSEC, page 72-79. ACM, (2014)A language-agnostic model for semantic source code labeling., , , , and . MASES@ASE, page 36-44. ACM, (2018)The Llama 3 Herd of Models., , , , , , , , , and 91 other author(s). CoRR, (2024)Improving Zero-Day Malware Testing Methodology Using Statistically Significant Time-Lagged Test Samples., and . CoRR, (2016)CrowdSource: Automated Inference of High Level Malware Functionality from Low-Level Symbols Using a Crowd Trained Machine Learning Model., , and . CoRR, (2016)