<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Security Patch Management on Qian Jia</title><link>https://qian-jia.github.io/tags/security-patch-management/</link><description>Recent content in Security Patch Management on Qian Jia</description><generator>Hugo -- 0.145.0</generator><language>en</language><lastBuildDate>Sun, 30 Mar 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://qian-jia.github.io/tags/security-patch-management/index.xml" rel="self" type="application/rss+xml"/><item><title>A Dynamic Patching Policy for Enterprise Information Systems</title><link>https://qian-jia.github.io/papers/dynamic-patching-policy/</link><pubDate>Sun, 30 Mar 2025 00:00:00 +0000</pubDate><guid>https://qian-jia.github.io/papers/dynamic-patching-policy/</guid><description>The present research proposes a novel dynamic patching policy (DPP) to minimize the total system cost.</description></item><item><title>Enterprise Security Patch Management with Deep Reinforcement Learning</title><link>https://qian-jia.github.io/papers/security-patch-rl-icis/</link><pubDate>Sun, 15 Dec 2024 00:00:00 +0000</pubDate><guid>https://qian-jia.github.io/papers/security-patch-rl-icis/</guid><description>This study addresses the patching decision problem by proposing a novel deep reinforcement learning-based approach. Presented in ICIS 2024.</description></item></channel></rss>