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Advanced Breach and Attack Simulation using ML

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Sat, Jul 6 2024

cybersecurity machine learning deep learning penetration testing threat modeling Generative Adversarial Networks GaN

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Extracting Semantic and Geometric Information in Images and Videos using GANs

Rameen Abdal, Ph.D. Student, Computer Science

Feb 15, 18:00 - 20:00

B1 L2 R2202

The success of Generative Adversarial Networks (GANs) has resulted in unprecedented quality both for image generation and manipulation. Recent state-of-the-art GANs (e.g., the StyleGAN series) have demonstrated outstanding results in photo-realistic image generation. In this dissertation, we explore the latent space properties, including image manipulation, extraction of 3D properties, and performing various weakly supervised and unsupervised downstream tasks using StyleGAN and its derivative architectures.