发表机构
Cybersecurity & Blockchain Research Group, i2CAT Foundation; Department of Information and Communication Technologies, Universitat Pompeu Fabra（网络安全与区块链研究组，i2CAT基金会; 信息与通信技术系，庞培法华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对缺乏网络安全知识团队强化本地IT环境难的问题，提出基于知识的安全DSS，利用统一数据集，基于MAID模型，通过无悔在线学习推荐安全控制子族，经不同数据集验证，取得较好性能和准确性。
AI 中文摘要
对于缺乏足够网络安全专业知识的团队来说，强化本地IT环境是一项艰巨的任务。在此方面，嵌入专家知识的决策支持系统（DSS）可通过安全建议指导用户实现目标。本文提出一种安全DSS，能根据用户对不同安全维度的最低覆盖要求推荐安全控制子族。它利用来自知名信息安全和学术来源的统一数据集。该DSS被定义为基于多智能体影响图（MAID）模型的非零和同步博弈，使用无悔在线学习探索7个安全维度的决策空间，以找到最符合要求且安全资源供应不足和过度供应最小的安全控制子族。通过不同数据集大小验证了其性能和准确性，使用约65%的软件可实现安全控制时满意度覆盖率达99%，运行时间为1.2 - 35.7秒；使用约29%的控制时覆盖率为73% - 77%，解决时间为0.8 - 13.8秒。
英文摘要
Hardening IT on-premises environments can be a daunting task for teams without access to adequate cybersecurity expertise. In this regard, Decision Support Systems (DSS) with embedded expert knowledge can assist users by guiding them with security recommendations to meet their objectives. This work proposes a Security DSS that recommends security control sub-families given minimal user requirements indicating coverage of different security dimensions. It leverages a curated, unified dataset from both well-known Information Security (InfoSec) and academic sources. This DSS is defined as a non-zero-sum, simultaneous game that is grounded in a Multi-Agent Influence Diagram (MAID) model and explores the decision space over 7 security dimensions or agents, using no-regret online learning to ultimately find the security control sub-families that best fit the requirements while incurring minimal under- and over-provisioning of security resources. This work was validated in terms of performance and accuracy, among others, for varying dataset sizes. It shows exceptional satisfaction coverage results of 99% when using as little as ~65% of the SW-implementable security controls, running in 1.2-35.7 seconds; and more moderate coverage results of 73%-77% when using ~29% of the controls, resolving in 0.8-13.8 seconds.
CommentsElsevier Knowledge-Based Systems (KNOSYS), 2026
DOI：10.1016/j.knosys.2026.116558
