Using Loops For Malware Classification Resilient to Feature-unaware Perturbations
Aravind Machiry, Nilo Redini, Eric Gustafson, Yanick Fratantonio, Yung Ryn Choe, Christopher Kruegel, Giovanni Vigna · 2018
In the past few years, both the industry and the academic communities have developed several approaches to detect malicious Android apps. State-of-the-art research approaches achieve very high accuracy when performing malware detection on existing datasets. These approaches perform their malware classification tasks in an "offline" scenario, where malware authors cannot learn from and adapt their malicious apps to these systems. In real-world deployments, however, adversaries get feedback about whether their app was detected, and can react accordingly by transforming their code until they are able to influence the classification.