Title: Evasion Attacks against Machine Learning at Test Time,
Abstract: 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 scenario, an adversary may attempt to evade a
deployed system at test time by carefully manipulating attack samples. In this
work, we present a simple but effective gradient-based approach that can be
exploited to systematically assess the security of several, widely-used
classification algorithms against evasion attacks. Following a recently
proposed framework for security evaluation, we simulate attack scenarios that
exhibit different risk levels for the classifier by increasing the attacker's
knowledge of the system and her ability to manipulate attack samples. This
gives the classifier designer a better picture of the classifier performance
under evasion attacks, and allows him to perform a more informed model
selection (or parameter setting). We evaluate our approach on the relevant
security task of malware detection in PDF files, and show that such systems can
be easily evaded. We also sketch some countermeasures suggested by our
analysis.