AGAPE: Anomaly Detection with Generative Adversarial Network for Improved Performance, Energy, and Security in Manycore Systems
AGAPE: Anomaly Detection with Generative Adversarial Network for Improved Performance, Energy, and Security in Manycore Systems
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DOI：
10.23919/date54114.2022.9774693
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发表时间：
2022-03
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影响因子：
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通讯作者：
Ke Wang;Hao Zheng;Yuan Li;Jiajun Li;A. Louri
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作者：
Ke Wang;Hao Zheng;Yuan Li;Jiajun Li;A. Louri
多核系统的安全性已经变得越来越重要。在片上系统（soc）中，硬件木马（ht）操纵路由组件的功能，使片上网络饱和，降低性能，并导致敏感数据泄漏。由于芯片上通信行为的日益动态和复杂性，现有的HT检测技术，包括运行时监测和最先进的基于学习的方法，无法及时准确地识别植入的HT。我们提出了AGAPE，一种新的基于生成对抗网络（GAN）的异常检测和缓解方法，用于安全的片上通信。AGAPE学习片上传感器在无高温和受高温感染工况下捕获的多个NoC属性的多变量时间序列分布。该GAN能够同时学习不同运行时属性之间的潜在交互，准确区分异常攻击情况和正常SoC行为，识别植入ht的类型和位置。利用检测结果，我们对检测到的每种类型的ht应用最合适的保护技术，而不是简单地隔离整个受ht感染的路由器，目的是减轻安全威胁并减少性能损失。仿真结果表明，与最先进的安全设计相比，AGAPE将HT检测精度提高了19%，将网络延迟和功耗分别降低了39%和30%。
The security of manycore systems has become increasingly critical. In system-on-chips (SoCs), Hardware Trojans (HTs) manipulate the functionalities of the routing components to saturate the on-chip network, degrade performance, and result in the leakage of sensitive data. Existing HT detection techniques, including runtime monitoring and state-of-the-art learning-based methods, are unable to timely and accurately identify the implanted HTs, due to the increasingly dynamic and complex nature of on-chip communication behaviors. We propose AGAPE, a novel Generative Adversarial Network (GAN)-based anomaly detection and mitigation method against HTs for secured on-chip communication. AGAPE learns the distribution of the multivariate time series of a number of NoC attributes captured by on-chip sensors under both HT-free and HT-infected working conditions. The proposed GAN can learn the potential latent interactions among different runtime attributes concurrently, accurately distinguish abnormal attacked situations from normal SoC behaviors, and identify the type and location of the implanted HTs. Using the detection results, we apply the most suitable protection techniques to each type of detected HTs instead of simply isolating the entire HT-infected router, with the aim to mitigate security threats as well as reducing performance loss. Simulation results show that AGAPE enhances the HT detection accuracy by 19%, reduces network latency and power consumption by 39% and 30%, respectively, as compared to state-of-the-art security designs.