Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Security has evolved into a totally different dimension with the ever-popular cloud computing, and with it comes a completely different exposure to cyber criminals who have become extra smart in what they do. Adversarial Machine Learning holds a proud position in achieving the very best of innovative defense methods, one that turned the tables on tactics that were originally designed to target systems. This paper takes you deep into the capabilities of AML that security teams can use to transform threat prediction, anomaly detection, and security control resiliency by tapping their strong, well-rounded capabilities, namely adversarial training, GANs, and reinforcement learning. Real-life implementations exemplify the fact that organizations can change the game only to go ahead of attackers after years of warding them off. The journey goes further to the very frontiers of innovation: federated learning methods that allow secure and un-data-leaking collaboration modes, quantum-resistant security mechanisms to ensure they are ready to face the computationally powerful threats of tomorrow, automated response systems that speed the resolution of incidents by factors of 10, even 100 or more, and human-AI cooperation that gets the best out of both sides. The security professionals will have a practical guide in terms of how to weave the adversarial techniques into the cloud defense they have, leading to the eventual establishment of systems that can adjust and fortify in the face of the threats they face, as opposed to crumbling under their weight.