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Delft University of Technology
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Data-driven models can outperform rule-based systems for IT incident prediction, even in highly regulated environments demanding auditability and explainability.
Despite using similar cryptographic protocols, popular messaging apps like Messenger and Telegram exhibit significantly larger attack surfaces and more aggressive network behavior than Signal, raising questions about their overall security and privacy posture.
LLM-powered security tools are surprisingly susceptible to confirmation bias, overlooking reintroduced vulnerabilities when pull requests are framed as security improvements.
Programmer attribution research is heavily skewed towards stylometric features and closed-world scenarios, leaving behavioral biometrics and open-world verification largely unexplored.
