39 citations · 68 across the 9 of their papers we have counts for
9 papers
HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models
Petr Simecek, Elnaz Babayeva, Jiri Balhar +23
LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs across 78 projects, including OpenS…
EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability
Andrea Ponte, Daniel Gibert, Matous Kozak +5
Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations (i) differ i…
Quality-Assured Fuzz Harness Generation via the Four Principles Framework
Ze Sheng, Dmitrijs Trizna, Luigino Camastra +3
Fuzz testing is the dominant technique for finding memory-safety vulnerabilities in C/C++ software, yet its effectiveness hinges on the quality of fuzz harnesses -- the programs th…
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines
Andrea Ponte, Dmitrijs Trizna, Luca Demetrio +3
As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia -- which pursue…
Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples
Matous Kozak, Luca Demetrio, Dmitrijs Trizna +1
Adversarial EXEmples are carefully-perturbed programs tailored to evade machine learning Windows malware detectors, with an ongoing effort to develop robust models able to address…
Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells
Dmitrijs Trizna, Luca Demetrio, Battista Biggio +1
Living-off-the-land (LOTL) techniques pose a significant challenge to security operations, exploiting legitimate tools to execute malicious commands that evade traditional detectio…
Nebula: Self-Attention for Dynamic Malware Analysis
Dmitrijs Trizna, Luca Demetrio, Battista Biggio +1
Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment and logging their actions. Previous work has proposed training machine learning…
Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations
Dmitrijs Trizna
We propose a hybrid machine learning architecture that simultaneously employs multiple deep learning models analyzing contextual and behavioral characteristics of Windows portable…
Shell Language Processing: Unix command parsing for Machine Learning
Dmitrijs Trizna
In this article, we present a Shell Language Preprocessing (SLP) library, which implements tokenization and encoding directed at parsing Unix and Linux shell commands. We describe…