24 citations · 48 across the 13 of their papers we have counts for
14 papers
Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness
Mashal Zainab, Salijona Dyrmishi, Hamid Bostani +2
Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and at…
REPLICANT: Learning Policies for Evading and Hardening Malware Detectors
Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia +5
To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-…
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…
ML-Based Behavioral Malware Detection Is Far From a Solved Problem
Yigitcan Kaya, Yizheng Chen, Marcus Botacin +5
Malware detection is a ubiquitous application of Machine Learning (ML) in security. In behavioral malware analysis, the detector relies on features extracted from program execution…
How to Train your Antivirus: RL-based Hardening through the Problem-Space
Ilias Tsingenopoulos, Jacopo Cortellazzi, Branislav Bošanský +5
ML-based malware detection on dynamic analysis reports is vulnerable to both evasion and spurious correlations. In this work, we investigate a specific ML architecture employed in…
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…
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…
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…
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Chao Shuai, Jieming Zhong, Shuang Wu +6
Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to ima…
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
Kun Pan, Yin Yifang, Yao Wei +6
The malicious use and widespread dissemination of deepfake pose a significant crisis of trust. Current deepfake detection models can generally recognize forgery images by training…
Dos and Don'ts of Machine Learning in Computer Security
Daniel Arp, Erwin Quiring, Feargus Pendlebury +5
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many diff…
Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]
Jacopo Cortellazzi, Feargus Pendlebury, Daniel Arp +3
Recent research efforts on adversarial machine learning (ML) have investigated problem-space attacks, focusing on the generation of real evasive objects in domains where, unlike im…