易于
发表于 2025-3-26 21:49:02
DAPT 2020 - Constructing a Benchmark Dataset for Advanced Persistent Threatstion faces, specifically attacks that go undetected by traditional signature-based intrusion detection systems. Along with the ability to process large amounts of data, machine learning brings the potential to detect contextual and collective anomalies, an essential attribute of an ideal threat dete
渐变
发表于 2025-3-27 02:50:23
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袭击
发表于 2025-3-27 07:02:27
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或者发神韵
发表于 2025-3-27 13:00:03
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雪崩
发表于 2025-3-27 15:23:08
Lecture Notes in Computer Sciencey malware. We also demonstrate the annotation process using MALOnt on exemplar threat intelligence reports. A work in progress, this research is part of a larger effort towards auto-generation of knowledge graphs for gathering malware threat intelligence from heterogeneous online resources.
Dissonance
发表于 2025-3-27 20:45:40
https://doi.org/10.1007/978-3-031-64483-2ned adversarial training and corresponding insights to efficiently make the model more robust on safety-critical scenarios. We hope this work can serve as a stepping stone to seek efficient defenses against adversarial examples in large-scale object detectors.
小官
发表于 2025-3-27 23:13:49
https://doi.org/10.1007/978-3-031-64171-8) systems, as well as recent advances in deep learning, we introduce a novel intrusion forecasting application. Using six months of data from a real, large organization, we demonstrate that this provides improved intrusion forecasting accuracy compared to existing methods.
Sigmoidoscopy
发表于 2025-3-28 05:10:37
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Champion
发表于 2025-3-28 06:43:39
Towards Practical Robustness Improvement for Object Detection in Safety-Critical Scenariosned adversarial training and corresponding insights to efficiently make the model more robust on safety-critical scenarios. We hope this work can serve as a stepping stone to seek efficient defenses against adversarial examples in large-scale object detectors.
inculpate
发表于 2025-3-28 13:22:54
Forecasting Network Intrusions from Security Logs Using LSTMs) systems, as well as recent advances in deep learning, we introduce a novel intrusion forecasting application. Using six months of data from a real, large organization, we demonstrate that this provides improved intrusion forecasting accuracy compared to existing methods.