56 citations · 94 across the 7 of their papers we have counts for
7 papers
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…
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers
Limin Yang, Zhi Chen, Jacopo Cortellazzi +5
Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibili…
Realizable Universal Adversarial Perturbations for Malware
Raphael Labaca-Castro, Luis Muñoz-González, Feargus Pendlebury +3
Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output. Universal Adversarial Perturbations (UAPs), whic…
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…
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
Federico Barbero, Feargus Pendlebury, Fabio Pierazzi +1
Machine learning for malware classification shows encouraging results, but real deployments suffer from performance degradation as malware authors adapt their techniques to evade d…
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…
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney +2
Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are com…