AGAPE: Anomaly Detection with Generative Adversarial Network for Improved Performance, Energy, and Security in Manycore Systems
Ke Wang1,a, Hao Zheng2, Yuan Li1,b, Jiajun Li1,c and Ahmed Louri1,d
1Dept. of Electrical and Computer Engineering, George Washington University, Washington, DC, USA
acory@gwu.edu
bliyuan5859@gwu.edu
clijiajun@gwu.edu
dlouri@gwu.edu
2Dept. of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida, USA
haozheng@gwu.edu
ABSTRACT
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.
Keywords: Manycore systems, Security, Hardware Trojans (HTs), Generative Adversarial Networks (GAN).
