889 citations · 1.9k 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…
Evasion Attacks against Machine Learning at Test Time
Battista Biggio, Igino Corona, Davide Maiorca +5
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack s…
Security Evaluation of Support Vector Machines in Adversarial Environments
Battista Biggio, Igino Corona, Blaine Nelson +5
Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering…
Bayesian Differential Privacy through Posterior Sampling
Christos Dimitrakakis, Blaine Nelson, and Zuhe Zhang +2
Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to p…
Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, Pavel Laskov
We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM's test error. Central…
Query Strategies for Evading Convex-Inducing Classifiers
Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang +4
Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade…