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Neuroattack: undermining spiking neural networks security through externally triggered bit-flips
- Source :
- 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), Jul 2020, Glasgow, United Kingdom. pp.1-8, ⟨10.1109/IJCNN48605.2020.9207351⟩, International Joint Conference on Neural Networks (IJCNN 2020), International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, United Kingdom. pp.1-8, ⟨10.1109/IJCNN48605.2020.9207351⟩, IJCNN
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injection based sneaky hardware backdoor through a carefully crafted adversarial input noise. Our results on Deep Neural Networks (DNNs) and SNNs show a serious integrity threat to state-of-the art machine-learning techniques.<br />Accepted for publication at the 2020 International Joint Conference on Neural Networks (IJCNN)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Cryptography and Security
Computer science
Distributed computing
Reliability (computer networking)
Machine Learning (stat.ML)
02 engineering and technology
01 natural sciences
SNN
Machine Learning (cs.LG)
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Machine Learning
Spiking Neural Networks
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
[SPI]Engineering Sciences [physics]
Hardware
Statistics - Machine Learning
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
[INFO]Computer Science [cs]
Fault-Injection Attacks
Resilience (network)
Adversarial Attacks
010302 applied physics
Spiking neural network
Deep Neural Networks
Resilience
Machine Learning, Spiking Neural Networks, Reliability, Adversarial Attacks, Fault-Injection Attacks, Deep Neural Networks, DNN, SNN, Security, Resilience, Cross-Layer
Biological system modeling
Reliability
020202 computer hardware & architecture
[SPI.TRON]Engineering Sciences [physics]/Electronics
Cross layer
Cross-Layer
Security
Noise (video)
Biological neural networks
Cryptography and Security (cs.CR)
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
DNN
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), Jul 2020, Glasgow, United Kingdom. pp.1-8, ⟨10.1109/IJCNN48605.2020.9207351⟩, International Joint Conference on Neural Networks (IJCNN 2020), International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, United Kingdom. pp.1-8, ⟨10.1109/IJCNN48605.2020.9207351⟩, IJCNN
- Accession number :
- edsair.doi.dedup.....58f4a4a435b4ab19fd54080e70ab9f7a