Global ETD Search
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Showing 1 to 11 of 11 for “"Machine Learning Security"”.
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Building and using robust representations in image classification
One of the major appeals of the deep learning paradigm is the ability to learn high-level feature representations of complex data. These learned representations obviate manual data pre-processing, and are versatile enough to generalize across tasks. However, they are not yet capable of fully …
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Towards Robust Deep Neural Networks
… enable state-of-the-art performance for most machine learning tasks. Unfortunately, they are vulnerable to attacks, such as Trojans during training and Adversarial Examples at test time. Adversarial Examples are inputs with carefully crafted perturbations added to benign samples. In the …
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Foresight: Countering Malware through Cooperative Forensics Sharing
… epidemic nature of the spreads limits the time security experts have to respond and be able to protect and fortify their systems. A pathogen might infect thousands of machines and cascade across the network producing consequences that could overwhelm the internet very quickly. Such attacks have …
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Certifiably trustworthy deep learning systems at scale
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Machine learning for security applications under dynamic and adversarial environments
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Enhancing Proof-of-Learning Security Against Spoofing Attacks Using Model Watermarking
<p>With the rapid expansion of machine learning (ML) technologies across diverse domains such as healthcare, finance, and autonomous systems, ensuring secure and trustworthy training methodologies has become more critical than ever. Proof-of-Learning (PoL) has recently emerged as a foundational …
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A study of security issues of mobile apps in the android platform using machine learning approaches
… traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile …
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NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images
Recent advancements in generative models have resulted in the improvement of hyper- realistic synthetic images or "deepfakes" at high resolutions, making them almost indistin- guishable from real images from cameras. While exciting, this technology introduces room for abuse. Deepfakes have already …
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Data-driven methods to improve resource utilization, fraud detection, and cyber-resilience in smart grids
… of generation and consumption, constructed using machine learning and statistical methods, improve resource utilization, fraud detection, and cyber-resilience in smart grids. The modern power grid, known as the smart grid, uses computer communication networks to improve efficiency by transporting …
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Robustifying Machine Learning based Security Applications
In recent years, machine learning (ML) has been explored and employed in many fields. However, there are growing concerns about the robustness of machine learning models. These concerns are further amplified in security-critical applications — attackers can manipulate the inputs (i.e., adversarial …
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Towards Secure and Resilient Machine Learning Systems
Over the past decade, Machine Learning (ML) technologies have undergone revolutionary advancements, extending beyond traditional domains such as computer vision (CV) and natural language processing (NLP). One of the most significant breakthroughs is the development of transformer models, which …