Rough seas are hampering efforts to salvage the boat:

The Speranza Marie, carrying 16,000 pounds of squid and some 1,000 gallons of diesel fuel, hit the shoreline near Chinese Harbor at about 2 a.m. on Dec. 15.

Six crew members were on board, and all were rescued without injury by another fishing boat.

[…]

However, large swells caused by the recent storm caused the Speranza Marie to pull loose from it anchored position and drift about 100 yards from from its original grounded location in Chinese Harbor, according to the Coast Guard.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

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From automation to insider threats, here’s what some experts at the Center for Internet Security predict will happen in 2023.[…]

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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 EarSpy can successfully detect word regions, time, and frequency domain features and generate a spectrogram for each word region. We train and test the extracted data using classical machine learning algorithms and convolutional neural networks. We found up to 98.66% accuracy in gender detection, 92.6% detection in speaker detection, and 56.42% detection in digit detection (which is 5X more significant than the random selection (10%)). Our result unveils the potential threat of eavesdropping on phone conversations from ear speakers using motion sensors.

It’s not great, but it’s an impressive start.

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