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Keeping Web-Browsing Data Safe From Hackers

Researchers find the root cause of side-channel attacks that are easy to implement but difficult to detect

Adam Zewe
Tue, 07/05/2022 - 12:02
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Malicious agents can use machine learning to launch powerful attacks that steal information in ways that are tough to prevent and often even more difficult to study.

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Attackers can capture data that “leak” between software programs running on the same computer. They then use machine-learning algorithms to decode those signals, which enables them to obtain passwords or other private information. These are called “side-channel attacks” because information is acquired through a channel not meant for communication.

Researchers at MIT have shown that machine learning-assisted side-channel attacks are both extremely robust and poorly understood. The use of machine-learning algorithms, which are often impossible to fully comprehend due to their complexity, is a particular challenge. In a new paper, the team studied a documented attack that was thought to work by capturing signals leaked when a computer accesses memory. They found that the mechanisms behind this attack were misidentified, which would prevent researchers from crafting effective defenses.

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