Security News
Abstract: Billions of dollars and countless GPU hours are currently spent on training Deep Neural Networks for a variety of tasks. Thus, it is essential to determine the difficulty of extracting all the parameters of such neural networks when given access to their black-box implementations.
Sponge Examples: Energy-Latency Attacks on Neural Networks shows how to find adversarial examples that cause a DNN to burn more energy, take more time, or both. They affect a wide range of DNN applications, from image recognition to natural language processing.
A group of researchers with CSIRO’s Data61, the digital innovation arm of Australia’s national science agency, have been working on a system for run time detection of trojan attacks on deep neural...
As CAPTCHA-haters know to their frequent irritation, the death of the text-based Completely Automated Procedures for Telling Computers and Humans Apart tends to be exaggerated.
Symantec introduced Industrial Control System Protection (ICSP) Neural, the neural network-integrated USB scanning station that helps organizations protect infrastructure by preventing the...
The company is rolling out a device that scans for malware on USB devices to block attacks on IoT and operational technology environments.
Kalray announced Kalray Target Controller (KTC80) with inline mirroring to offload the network while securing the data and the Kalray Neural Network (KaNN) performing live object recognition using...
A team of Microsoft researchers has been working on improving fuzzing techniques by using deep neural networks, and initial tests have shown promising results. read more
Researchers have trained a neural network to encrypt its communications. In their experiment, computers were able to make their own form of encryption using machine learning, without being taught...
Neural networks are good at identifying faces, even if they're blurry: In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects...