Deep Learning | Breaking Cybersecurity News | The Hacker News

Google has announced that it's open-sourcing  Magika , an artificial intelligence (AI)-powered tool to identify file types, to help defenders accurately detect binary and textual file types. "Magika outperforms conventional file identification methods providing an overall 30% accuracy boost and up to 95% higher precision on traditionally hard to identify, but potentially problematic content such as VBA, JavaScript, and Powershell," the company  said . The software uses a "custom, highly optimized deep-learning model" that enables the precise identification of file types within milliseconds. Magika implements inference functions using the Open Neural Network Exchange ( ONNX ). Google said it internally uses Magika at scale to help improve users' safety by routing Gmail, Drive, and Safe Browsing files to the proper security and content policy scanners. In November 2023, the tech giant unveiled  RETVec  (short for Resilient and Efficient Text Vectorizer),...

Medieval castles stood as impregnable fortresses for centuries, thanks to their meticulous design. Fast forward to the digital age, and this medieval wisdom still echoes in cybersecurity. Like castles with strategic layouts to withstand attacks, the Defense-in-Depth strategy is the modern counterpart — a multi-layered approach with strategic redundancy and a blend of passive and active security controls.  However, the evolving cyber threat landscape can challenge even the most fortified defenses. Despite the widespread adoption of the Defense-in-Depth strategy, cyber threats persist. Fortunately, the Defense-in-Depth strategy can be augmented using Breach and Attack Simulation (BAS), an automated tool that assesses and improves every security control in each layer. Defense-in-Depth: False Sense of Security with Layers Also known as multi-layered defense, the defense-in-depth strategy has been widely adopted by organizations since the early 2000s. It's based on the assumption th...

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A group of academics has devised a "deep learning-based acoustic side-channel attack" that can be used to classify laptop keystrokes that are recorded using a nearby phone with 95% accuracy. "When trained on keystrokes recorded using the video conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium," researchers Joshua Harrison, Ehsan Toreini, and Maryam Mehrnezhad  said  in a new study published last week. Side-channel attacks  refer  to a class of  security exploits  that aim to glean insights from a system by monitoring and measuring its physical effects during the processing of sensitive data. Some of the common observable effects include runtime behavior, power consumption, electromagnetic radiation, acoustics, and cache accesses. Although a completely side-channel-free implementation does not exist, practical attacks of this kind can have damaging consequences for user privacy and security as they could be weaponize...