4 citations · 4 across the 2 of their papers we have counts for
7 papers
Unraveling the Key of Machine Learning-based Android Malware Detection
Jiahao Liu, Jun Zeng, Fabio Pierazzi +3
With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patter…
Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
Theo Chow, Mario D'Onghia, Lorenz Linhardt +4
The reliability of machine learning critically depends on dataset quality. While machine learning applied to computer vision and natural language processing benefits from high-qual…
On the Effectiveness of Adversarial Training on Malware Classifiers
Hamid Bostani, Jacopo Cortellazzi, Daniel Arp +3
Adversarial Training (AT) is a key defense against Machine Learning evasion attacks, but its effectiveness for real-world malware detection remains poorly understood. This uncertai…
On the Lack of Robustness of Binary Function Similarity Systems
Gianluca Capozzi, Tong Tang, Jie Wan +5
Binary function similarity, which often relies on learning-based algorithms to identify what functions in a pool are most similar to a given query function, is a sought-after topic…
The Adaptive Arms Race: Redefining Robustness in AI Security
Ilias Tsingenopoulos, Vera Rimmer, Davy Preuveneers +3
Despite considerable efforts on making them robust, real-world AI-based systems remain vulnerable to decision based attacks, as definitive proofs of their operational robustness ha…
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
Zeliang Kan, Shae McFadden, Daniel Arp +5
Machine learning (ML) plays a pivotal role in detecting malicious software. Despite the high F1-scores reported in numerous studies reaching upwards of 0.99, the issue is not compl…