Yet another smartphone side-channel attack: “EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers“: Abstract: Eavesdropping from the user’s smartphone is a well-known threat to the user’s safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors. In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feasibility of using motion sensors to capture such tiny speech vibrations. We investigate the impacts of these new ear speakers on built-in motion sensors and examine the potential to elicit private speech information from the minute vibrations. Our designed system ...

An AI chatbot wrote the following article on AI in cybersecurity. No humans were harmed in the drafting of this article. Artificial intelligence (AI) and machine learning (ML) are rapidly advancing technologies that have the potential to greatly impact cybersecurity. These technologies can be used to enhance security by analyzing large amounts of data and identifying patterns that may indicate a potential threat, allowing organizations to take proactive measures to prevent attacks. However, they also present their own set of challenges, as they can be used by attackers to automate and scale their attacks.

… are inputs designed to maximise energy consumption and latency, to drive machine learning (ML) systems towards their worst-case performance.

IT teams responsible for infrastructure monitoring, security, service availability, and help desks.

Artificial intelligence (AI) in cybersecurity can be a double-edged sword. While AI can effectively mitigate threats and prevent potential cyberattacks, criminals can also exploit the technology to their advantage – putting businesses and customers at significant risk. We're still dealing with the side effects of COVID-19, not only the pandemic itself but also the increased cases of cybercrimes happening worldwide. Cyberattacks are on the rise, with the recent Colonial Pipeline and Pulse Secure VPN attacks being added to SolarWinds and Microsoft Exchange Server as notable attacks with far reaching consequences. In 2020, the United States experienced 1001 data breach cases, and 155.8 million individuals faced data exposure (accidentally revealing sensitive information).

Companies are interested in buying insurance to mitigate the risk of adoption and deployment of new AI applications with no history of use. "When it comes to the commercial use of AI, businesses can't rely on government regulation to protect them against potential losses in the event it fails to live up to its promise," stated Saar Yoskovitch, CEO and cofounder of Augury, in a recent account in Open Access Government. As deployed AI systems mature, they will increasingly make high risk decisions. "But AI models are often brittle, do not deal well with edge cases and may have been trained on a dataset with inherent biases," stated Yoskovitch. This is especially prevalent with AI systems that use human behavior as an input, such as auto insurance applications that capture an individual customer's driving behavior.

Machine Learning is ingrained in our day-to-day life. It is part of our spam filters mechanism, voice command smartphone interpretation and any search on Google. Alexa, what time is it? Chances are good that machine learning has been helping you along somewhere in your life. This is a short blog on Machine Learning 101.

Security leaders are increasingly turning to AI and ML-based defenses against cyberattacks as pessimism grows over the efficacy of human-based cybersecurity defense efforts. A recent survey from MIT Technology Review Insights, sponsored by Darktrace, found more than half of business leaders think security strategies based on human-led responses to fast-moving attacks are failing; nearly all have begun to bolster their defenses in preparation for AI-enabled attacks. "Cyber AI autonomously stops threats in their tracks and surfaces relevant information in a digestible narrative, augmenting human teams and giving them time to focus on strategic tasks that matter," said Darktrace's director of threat hunting, Max Heinemeyer. "All that organizations can do to prepare is simply embrace self-learning AI as a force multiplier." He noted that AI-powered cybersecurity platforms can integrate with other tools in a security toolbox, ingest new forms of telemetry from existing investments for further enrichment, share detections and incidents with workflow tools and even orchestrate response actions across the rest of the digital estate, for example, by integrating with preventative tools.

Many machine learning (ML) models are Python pickle files under the hood, and it makes sense. The use of pickling conserves memory, enables start-and-stop model training, and makes trained models portable (and, thereby, shareable). Pickling is easy to implement, is built into Python without requiring additional dependencies, and supports serialization of custom objects. There's little doubt about why choosing pickling for persistence is a popular practice among Python programmers and ML practitioners. Pre-trained models are typically treated as "free" byproducts of ML since they allow the valuable intellectual property like algorithms and corpora that produced the model to remain private.

Artificial Intelligence is taking K-12 cyber safety monitoring to the next level Issues with cyber safety in schools are on the rise. Gaggle recently reported a 66% jump in the number of cyber safety incidents in the first three months of the 2020-21 school year compared to the same time in the 2019-20 school year. The post How Cyber Safety Artificial Intelligence Helps Students In K-12 Technology appeared first on ManagedMethods.