标题： 基于多种缓存侧信道攻击检测的混合深度学习模型：比较分析 显示英文标题

标题： Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks Detection: A Comparative Analysis

摘要： 缓存侧信道攻击是一种复杂且持续的威胁，利用现代处理器中的漏洞来提取敏感信息。 这些攻击利用共享计算资源中的弱点，尤其是最后一级缓存，以推断数据访问和执行流程中的模式，通常可以绕过传统的安全防御措施。 这类攻击尤其危险，因为它们可以在不需物理访问受害者设备的情况下远程执行。 本研究关注此类威胁的一个特定类别：指纹攻击，其中攻击者通过缓存侧信道监控和分析共存进程的行为。 这可能会揭示机密信息，例如加密密钥或用户活动模式。 一个全面的威胁模型说明了攻击者如何与目标系统共享计算资源，并利用这些侧信道来破坏敏感数据。 为了缓解此类风险，提出了一种混合深度学习模型用于检测缓存侧信道攻击。 其性能与五种广泛使用的深度学习模型进行了比较：多层感知器、卷积神经网络、简单循环神经网络、长短期记忆网络和门控循环单元。 实验结果表明，该混合模型的检测率高达99.96%。 这些发现突显了现有模型的局限性，增强了防御机制的必要性，并指明了未来研究的方向，以保护敏感数据免受不断演变的侧信道威胁。 显示英文摘要

摘要： Cache side channel attacks are a sophisticated and persistent threat that exploit vulnerabilities in modern processors to extract sensitive information. These attacks leverage weaknesses in shared computational resources, particularly the last level cache, to infer patterns in data access and execution flows, often bypassing traditional security defenses. Such attacks are especially dangerous as they can be executed remotely without requiring physical access to the victim's device. This study focuses on a specific class of these threats: fingerprinting attacks, where an adversary monitors and analyzes the behavior of co-located processes via cache side channels. This can potentially reveal confidential information, such as encryption keys or user activity patterns. A comprehensive threat model illustrates how attackers sharing computational resources with target systems exploit these side channels to compromise sensitive data. To mitigate such risks, a hybrid deep learning model is proposed for detecting cache side channel attacks. Its performance is compared with five widely used deep learning models: Multi-Layer Perceptron, Convolutional Neural Network, Simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit. The experimental results demonstrate that the hybrid model achieves a detection rate of up to 99.96%. These findings highlight the limitations of existing models, the need for enhanced defensive mechanisms, and directions for future research to secure sensitive data against evolving side channel threats.