Attack-detection mechanisms should be able to reliably detect, predict, and understandably explain both known and novel threats, while also being able to cope with evasion techniques (like obfuscation, polymorphism, or stealth low-volume attacks). They should not just be able to detect any critical incident, but also raise alarms when it matters only; the more false alarms are raised, the higher the risk that users will ignore warnings or even disable the detection permanently. Moreover, to provide a strong line of defense, attack detection methods should be complemented by suitable defensive technologies, ideally selected autonomously by the system under attack, to counter whatever threat it encounters. In this research area we pursue this ideal, currently amongst others by focusing on the detection and analysis of modern malware, on the prevention and attribution of DDoS attacks, and on the identification and mitigation of novel system vulnerabilities.
IEEE European Symposium on Security and Privacy (EuroS&P)
SoK: A Systematic Review of Integration and Reproducibility of Fuzzing Research into AFL++
IEEE Cybersecurity Development (SecDev)
SoK: A Modularized Framework for Symbolic Execution and Application for Usable Tool Design
IEEE Symposium on Security and Privacy (S&P)
Crucible: Retrofitting Commodity CPUs with Vulnerabilities via Transparent Software Emulation
IEEE Symposium on Security and Privacy (S&P)
SmuFuzz: Enable Deep System Management Mode Fuzzing in Fully Featured UEFI Runtime Environment
IEEE Symposium on Security and Privacy (S&P)
RISCy Cache Coherence: Timer-Free Architectural Cache Attacks via Instruction/Data Cache Incoherence
