  To cope with the increasing variability and sophistication of modern attacks,
machine learning has been widely adopted as a statistically-sound tool for
malware detection. However, its security against well-crafted attacks has not
only been recently questioned, but it has been shown that machine learning
exhibits inherent vulnerabilities that can be exploited to evade detection at
test time. In other words, machine learning itself can be the weakest link in a
security system. In this paper, we rely upon a previously-proposed attack
framework to categorize potential attack scenarios against learning-based
malware detection tools, by modeling attackers with different skills and
capabilities. We then define and implement a set of corresponding evasion
attacks to thoroughly assess the security of Drebin, an Android malware
detector. The main contribution of this work is the proposal of a simple and
scalable secure-learning paradigm that mitigates the impact of evasion attacks,
while only slightly worsening the detection rate in the absence of attack. We
finally argue that our secure-learning approach can also be readily applied to
other malware detection tasks.
