Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output.
We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.
Full control over a Wi-Fi chip for research purposes is often limited by its firmware, which makes it hard to evolve communication protocols and test schemes in practical environments.
Other Computer Science
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
In this work, we propose a novel framework to identify and mitigate a recently disclosed covert channel scheme exploiting unprotected broadcast messages in cellular MAC layer protocols.
Cryptography and Security Information Theory Networking and Internet Architecture Information Theory
Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment.
Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns.
Software Engineering Cryptography and Security
Nevertheless, the challenge discussed in the paper is the issue of network connectivity for such solutions.
In this paper, we present our customised deep neural network model, we review the research gaps, the existing challenges, and the solutions to cope with the issues.
In this new paradigm, AI frameworks such as TensorFlow and PyTorch play a key role, which is as essential as the compiler for traditional programs.