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A vulnerability (CVE-2023-4211) in the kernel drivers for several Mali GPUs “may be under limited, targeted exploitation,” British semiconductor manufacturer Arm has confirmed on Monday, when it released drivers updated with patches. Arm…
Some customers claim titanium frame of more expensive models becomes ‘so hot it’s too hot to hold’Apple is facing complaints from users about overheating in relation to its new iPhone 15 models, with some customers claiming the titanium frame becomes t…
Leaked internal messages detail efforts by the Chinese tech giant to court Greek officials and fight an American-led effort against its technology.
Leaked internal messages detail efforts by the Chinese tech giant to court Greek officials and fight an American-led effort against its technology.
Mobile Verification Toolkit (MVT) is a collection of utilities to simplify and automate the process of gathering forensic traces helpful to identify a potential compromise of Android and iOS devices. MVT supports using public indicators of compromise (…
Apple disputing tests by watchdog that showed radio frequency exposure was higher than permittedApple is facing a ban on the sale and potential recall of its iPhone 12 in France, as the US company contests a report that the smartphone exceeds radio fre…
Apple shares fall as local government and state-owned firms latest to face restrictions on devices at workChina’s government has reportedly expanded its ban of iPhones to local government workers and state-owned companies, a day after it emerged centra…
The release of a homegrown Chinese smartphone during a visit by the Biden official in charge of regulating such technology shows the U.S.-China tech conflict is alive and well.
ESET researchers have identified two active campaigns targeting Android users, where the threat actors behind the tools for Telegram and Signal are attributed to the China-aligned APT group GREF. Most likely active since July 2020 and since July 2022, …
Researchers have trained a ML model to detect keystrokes by sound with 95% accuracy.
“A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards”
Abstract: With recent developments in deep learning, the ubiquity of microphones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever. This paper presents a practical implementation of a state-of-the-art deep learning model in order to classify laptop keystrokes, using a smartphone integrated microphone. When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%, the highest accuracy seen without the use of a language model. When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms. We discuss a series of mitigation methods to protect users against these series of attacks…