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		<title>Adaptive AI framework keeps intrusion detection accurate as data drifts</title>
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		<pubDate>Tue, 27 Oct 2026 08:12:20 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Adaptive Learning]]></category>
		<category><![CDATA[concept drift]]></category>
		<category><![CDATA[drift detection]]></category>
		<category><![CDATA[Explainable AI]]></category>
		<category><![CDATA[KDD Cup 1999]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[network intrusion detection]]></category>
		<category><![CDATA[SHAP]]></category>
		<category><![CDATA[TEAL-XAI]]></category>
		<category><![CDATA[temporal weighting]]></category>
		<category><![CDATA[UNSW-NB15]]></category>
		<category><![CDATA[XGBoost]]></category>
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					<description><![CDATA[A new adaptive learning framework combines temporal decay weighting, sliding-window retraining and closed-loop SHAP explanations to keep intrusion detection accurate under concept drift.]]></description>
		
		
		
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