282 citations · 552 across the 7 of their papers we have counts for
7 papers
Support Vector Machines under Adversarial Label Contamination
Huang Xiao, Battista Biggio, Blaine Nelson +3
Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attac…
hybrid-Falcon: Hybrid Pattern Malware Detection and Categorization with Network Traffic and Program Code
Peng Xu, Claudia Eckert, Apostolis Zarras
Nowadays, Android is the most dominant operating system in the mobile ecosystem, with billions of people using its apps daily. As expected, this trend did not go unnoticed by miscr…
Analyzing Control Flow Integrity with LLVM-CFI
Paul Muntean, Matthias Neumayer, Zhiqiang Lin +3
Control-flow hijacking attacks are used to perform malicious com-putations. Current solutions for assessing the attack surface afteracontrol flow integrity(CFI) policy was applied…
IntRepair: Informed Repairing of Integer Overflows
Paul Muntean, Martin Monperrus, Hao Sun +2
Integer overflows have threatened software applications for decades. Thus, in this paper, we propose a novel technique to provide automatic repairs of integer overflows in C source…
Is feature selection secure against training data poisoning?
Huang Xiao, Battista Biggio, Gavin Brown +3
Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation o…
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio +4
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fie…