Privacy-Preserving in Blockchain-based Federated Learning Systems

no code implementations • 7 Jan 2024 • Sameera K. M., Serena Nicolazzo, Marco Arazzi, Antonino Nocera, Rafidha Rehiman K. A., Vinod P, Mauro Conti

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models.

Federated Learning Privacy Preserving

GANG-MAM: GAN based enGine for Modifying Android Malware

no code implementations • 27 Sep 2021 • Renjith G, Sonia Laudanna, Aji S, Corrado Aaron Visaggio, Vinod P

Malware detectors based on machine learning are vulnerable to adversarial attacks.

Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach

no code implementations • 20 Apr 2019 • Rahim Taheri, Reza Javidan, Mohammad Shojafar, Vinod P, Mauro Conti

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.

BIG-bench Machine Learning Data Poisoning +2

FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware

no code implementations • 17 Sep 2018 • Deepa K, Radhamani G, Vinod P, Mohammad Shojafar, Neeraj Kumar, Mauro Conti

Ever increasing number of Android malware, has always been a concern for cybersecurity professionals.

Attribute feature selection +1