  The widespread adoption of smartphones dramatically increases the risk of
attacks and the spread of mobile malware, especially on the Android platform.
Machine learning-based solutions have been already used as a tool to supersede
signature-based anti-malware systems. However, malware authors leverage
features from malicious and legitimate samples to estimate statistical
difference in-order to create adversarial examples. Hence, to evaluate the
vulnerability of machine learning algorithms in malware detection, we propose
five different attack scenarios to perturb malicious applications (apps). By
doing this, the classification algorithm inappropriately fits the discriminant
function on the set of data points, eventually yielding a higher
misclassification rate. Further, to distinguish the adversarial examples from
benign samples, we propose two defense mechanisms to counter attacks. To
validate our attacks and solutions, we test our model on three different
benchmark datasets. We also test our methods using various classifier
algorithms and compare them with the state-of-the-art data poisoning method
using the Jacobian matrix. Promising results show that generated adversarial
samples can evade detection with a very high probability. Additionally, evasive
variants generated by our attack models when used to harden the developed
anti-malware system improves the detection rate up to 50% when using the
Generative Adversarial Network (GAN) method.
