7 citations · 7 across the 1 of their papers we have counts for
3 papers
cs.CR2021★ 7 cited
GANG-MAM: GAN based enGine for Modifying Android Malware
Renjith G, Sonia Laudanna, Aji S +2
Malware detectors based on machine learning are vulnerable to adversarial attacks. Generative Adversarial Networks (GAN) are architectures based on Neural Networks that could produ…
cs.CR2019
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach
Rahim Taheri, Reza Javidan, Mohammad Shojafar +2
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 solut…
cs.CR2018
FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware
Deepa K, Radhamani G, Vinod P +3
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic a…