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		<title>Teaching AI Defenses to Survive Attacks With Just Three Examples</title>
		<link>https://bioengineer.org/teaching-ai-defenses-to-survive-attacks-with-just-three-examples/</link>
		
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		<pubDate>Sat, 10 Oct 2026 22:08:35 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[adversarial attacks]]></category>
		<category><![CDATA[adversarial training]]></category>
		<category><![CDATA[cybersecurity]]></category>
		<category><![CDATA[few-shot learning]]></category>
		<category><![CDATA[FGSM]]></category>
		<category><![CDATA[Internet of Things]]></category>
		<category><![CDATA[intrusion detection]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[network security]]></category>
		<category><![CDATA[PGD]]></category>
		<category><![CDATA[prototypical networks]]></category>
		<category><![CDATA[resource-constrained devices]]></category>
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					<description><![CDATA[Researchers have combined prototypical few-shot learning with adversarial training to build an IoT intrusion detection system that learns attacks from three examples per class and retains 80 to 90 percent accuracy under white-box adversarial attacks.]]></description>
		
		
		
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