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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TypeNet: Deep Learning Keystroke Biometrics · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2101.05570/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2101.05570/feed.xml" rel="self" type="application/rss+xml" /><item><title>TypeNet: Deep Learning Keystroke Biometrics — mentioned by walterbell</title><link>https://news.ycombinator.com/item?id=37758894</link><guid isPermaLink="false">urn:hackerpapers:mention:2101.05570:37758894</guid><pubDate>Tue, 03 Oct 2023 23:21:41 GMT</pubDate><description>&lt;p&gt;Yes, it&amp;#x27;s unclear how the FCC is allowing IEEE 802.11bf to proceed if it can be used to collect passwords and other typed data, not to mention what people are physically doing in different rooms of their homes and businesses. Good for vendors of 2FA and faraday rooms, but bad for the millions of buildings about to become transparent. As for keystroke timing, it could theoretically have been collected for years by local and web (search?) services which offered autocomplete. Research and investment is ongoing, e.g. https://arxiv.org/pdf/2101.05570 &amp;gt; Our approach called TypeNet achieves…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2101.05570/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
