迈向负责任的人工智能增强网络防御：模式识别、纵深防御及人机协作的论证
Toward Responsible AI-Augmented Cyber Defense: Pattern Recognition, Defense-in-Depth, and the Case for Human-AI Collaboration
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中文总结 AI 辅助
本文提出一个可证伪模型，形式化纵深防御、AI模式识别与人机协作，通过模拟证明AI增强在分层饱和处收益最大，且全面人工审查非最优，为平衡人机协作提供精确设计目标。
中文摘要 AI 辅助
网络安全文献已广泛记录了人工智能（AI）在威胁检测、事件响应和预防方面的运营效益，同时也提出了关于过度自动化、算法偏见和分析师技能退化的定性担忧。然而，目前仍缺乏一个正式的、可证伪的模型，将文献中反复出现的三个概念联系起来：纵深防御理论、人工智能模式识别理论以及安全运营中的人机协作。本文开发了这样一个模型。我们将分层防御形式化为伯努利检测级联，其中AI增强以乘法方式跨层介入；我们将每层的模式识别行为形式化为Neyman-Pearson/贝叶斯检测器，并推导出闭式最优阈值；我们将人机分诊形式化为容量受限的级联，并在检测概率与误报（“警报疲劳”）率之间进行明确、可量化的权衡。在说明性但现实的操作点上进行的蒙特卡洛/解析模拟表明：（i）AI增强在防御层间复合，恰好在传统分层饱和之处带来最大的边际收益；（ii）对AI标记的警报进行全面人工审查并非最优：将分析师容量提高到100%覆盖率可将误报减少约20倍，但同时会降低系统级检测概率，因为不完美的分析师准确性此时应用于每个警报而非经过筛选的子集。这些结果为广泛重复的“平衡人机协作”的定性建议提供了精确、可测试的形式，并提出了一个内部最优容量比作为安全运营中心（SOC）的具体设计目标，包括保护IT/OT融合关键基础设施的SOC。
英文摘要
Cybersecurity literature has extensively documented the operational benefits of artificial intelligence (AI) for threat detection, incident response, and prevention, while raising qualitative concerns about over-automation, algorithmic bias, and analyst-skill erosion. What remains largely absent is a formal, falsifiable model connecting three constructs that recur across this literature: Defense-in-Depth Theory, the Artificial Intelligence Theory of Pattern Recognition, and human-AI collaboration in security operations. This paper develops such a model. We formalize layered defense as a Bernoulli detection cascade in which AI augmentation enters multiplicatively across layers; we formalize each layer's pattern-recognition behavior as a Neyman-Pearson/Bayesian detector with a derived closed-form optimal threshold; and we formalize human-AI triage as a capacity-constrained cascade with an explicit, quantifiable trade-off between detection probability and false-alarm ("alert fatigue") rate. A Monte Carlo/analytical simulation evaluated at illustrative but realistic operating points shows that (i) AI augmentation compounds across defense layers, delivering its largest marginal gains exactly where traditional layering saturates, and (ii) full human review of AI-flagged alerts is not optimal: increasing analyst capacity toward 100% coverage cuts false alarms by roughly 20-fold but simultaneously lowers system-level detection probability, because imperfect analyst accuracy is then applied to every alert rather than a filtered subset. These results give the widely repeated qualitative recommendation of "balanced human-AI collaboration" a precise, testable form and suggest an interior-optimum capacity ratio as a concrete design target for security operations centers (SOCs), including those securing IT/OT-converged critical infrastructure.
发表机构
- Daw Alfada Company（道阿尔法达公司）
- University of Misan（米桑大学）
机构由 AI 辅助整理，请以论文原文为准。
