Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
It addresses security risks in machine learning for malware detection, offering a practical solution for cybersecurity applications, though it is incremental as it builds on existing attack frameworks.
The paper tackles the vulnerability of machine learning-based Android malware detectors to evasion attacks, proposing a secure-learning paradigm that mitigates attack impact with only a slight decrease in detection performance.
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.