发表机构
The University of Texas at Dallas（德克萨斯大学达拉斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建了保留真实ACN会话的泄漏控制会话级基准，提出双分支Masked-AE转换增强模型，在多类模型对比中实现最强鲁棒验证性能，可检测电动汽车充电基础设施的恶意请求操纵且不拒绝合法用户选择。
AI 中文摘要
电动汽车充电基础设施中的网络攻击检测因合法的激活后请求能量和出发时间修订而变得复杂。充电操纵攻击可利用相同的接口和变量，因此仅检测请求变更无法确定恶意意图。本文开发了一种泄漏控制的会话级基准，其保留了真实自适应充电网络（Adaptive Charging Network, ACN）会话的有序输入，并将合法修订建模为正常行为。固定池将每个生成的攻击保留在其源会话的拆分中，包含6种基于物理动机的攻击及其协同变体。我们在公共源分组折叠、攻击数据和操作约束下，比较了22种仅轮廓、感知转换和上下文分层的模型族。所提出的双分支掩码自编码器（Masked-AE）转换增强模型评估当前请求是否正常，以及其产生的转换是否类似观察到的良性更新。其状态分支结合了掩码重建与径向基函数单类支持边界，而其转换分支结合了掩码重建与收缩协方差距离。源分组五折交叉验证在明确的整体正常和良性更新接受约束下选择完整配置；不相交的正常数据在校准最终阈值后进行一次测试评估。所开发的双分支模型提供了最强的鲁棒验证性能，同时检测恶意请求操纵，且不会学习拒绝合法用户选择。
英文摘要
Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and departure time. Charging manipulation attacks can exploit the same interface and variables; therefore, detecting a request change alone does not establish malicious intent. This paper develops a leakage-controlled session-level benchmark that preserves the ordered inputs of real Adaptive Charging Network (ACN) sessions and models legitimate revisions as normal behavior. A fixed pool keeps each generated attack in its source session's split and contains six physically motivated attacks and their coordinated variants. We compare 22 profile-only, transition-aware, and context-stratified model families under common source-grouped folds, attack data, and operating constraints. The proposed Dual-Branch Masked-Autoencoder (Masked-AE) Transition Boost model evaluates whether the current request is normal and whether its producing transition resembles an observed benign update. Its state branch combines masked reconstruction with a radial-basis-function one-class support boundary, while its transition branch combines masked reconstruction with shrinkage covariance distance. Source-grouped five-fold cross-validation selects complete configurations under explicit overall-normal and benign-update acceptance constraints; disjoint normal data then calibrate the final threshold before one test evaluation. The developed dual-branch model provides the strongest robust validation performance while detecting malicious request manipulations without learning to reject legitimate user choices.
