Institute of Computing Technology, Chinese Academy IR
Variation Enhanced Attacks Against RRAM-Based Neuromorphic Computing System | |
Lv, Hao1; Li, Bing2; Zhang, Lei1; Liu, Cheng3; Wang, Ying3 | |
2023-05-01 | |
发表期刊 | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
![]() |
ISSN | 0278-0070 |
卷号 | 42期号:5页码:1588-1596 |
摘要 | The RRAM-based neuromorphic computing system (NCS) has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and thus being widely used in many data-centric applications. The reliability and security issues of the NCS, therefore, become an essential problem. In this article, we systematically investigated the adversarial threats to the RRAM-based NCS and observed that the RRAM hardware feature can be leveraged to strengthen the attack effect, which has not been granted sufficient attention by previous algorithmic attack methods. Thus, we proposed two types of hardware-aware attack methods with respect to different attack scenarios and objectives. The first is an adversarial attack, VADER, which perturbs the input samples to mislead the prediction of neural networks. The second is fault injection attack, EFI, which perturbs the network parameter space such that a specified sample will be classified to a target label, while maintaining the prediction accuracy on other samples. Both attack methods leverage the RRAM properties to improve the performance compared with the conventional attack methods. Experimental results show that our hardware-aware attack methods can achieve nearly 100% attack success rate with extremely low operational cost, while maintaining the attack stealthiness. |
关键词 | Security Hardware Neuromorphic engineering Computational modeling Circuit faults Resistance Immune system Adversarial attack fault injection attack neuromorphic computing system (NCS) processing in memory reliability resistive memory |
DOI | 10.1109/TCAD.2022.3207316 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China (NSFC)[61874124] ; National Natural Science Foundation of China (NSFC)[62204164] ; National Natural Science Foundation of China (NSFC)[62222411] ; Zhejiang Lab[2021PC0AC01] ; Beijing Natural Science Foundation[4194092] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000976102300017 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/21440 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Wang, Ying |
作者单位 | 1.Chinese Acad Sci, Univ Chinese Acad Sci, Inst Comp Technol, Beijing 100089, Peoples R China 2.Capital Normal Univ, Acad Multidisciplinary Studies, Beijing 100048, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100089, Peoples R China |
推荐引用方式 GB/T 7714 | Lv, Hao,Li, Bing,Zhang, Lei,et al. Variation Enhanced Attacks Against RRAM-Based Neuromorphic Computing System[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2023,42(5):1588-1596. |
APA | Lv, Hao,Li, Bing,Zhang, Lei,Liu, Cheng,&Wang, Ying.(2023).Variation Enhanced Attacks Against RRAM-Based Neuromorphic Computing System.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,42(5),1588-1596. |
MLA | Lv, Hao,et al."Variation Enhanced Attacks Against RRAM-Based Neuromorphic Computing System".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 42.5(2023):1588-1596. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论