Computer Science > Cryptography and Security
arXiv:2311.16684 (cs)
[Submitted on 28 Nov 2023]
Title:A Unified Hardware-based Threat Detector for AI Accelerators
View a PDF of the paper titled A Unified Hardware-based Threat Detector for AI Accelerators, by Xiaobei Yan and 2 other authors
View PDFAbstract:The proliferation of AI technology gives rise to a variety of security threats, which significantly compromise the confidentiality and integrity of AI models and applications. Existing software-based solutions mainly target one specific attack, and require the implementation into the models, rendering them less practical. We design UniGuard, a novel unified and non-intrusive detection methodology to safeguard FPGA-based AI accelerators. The core idea of UniGuard is to harness power side-channel information generated during model inference to spot any anomaly. We employ a Time-to-Digital Converter to capture power fluctuations and train a supervised machine learning model to identify various types of threats. Evaluations demonstrate that UniGuard can achieve 94.0% attack detection accuracy, with high generalization over unknown or adaptive attacks and robustness against varied configurations (e.g., sensor frequency and location).
| Subjects: | Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2311.16684 [cs.CR] |
| (orarXiv:2311.16684v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2311.16684 arXiv-issued DOI via DataCite |
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled A Unified Hardware-based Threat Detector for AI Accelerators, by Xiaobei Yan and 2 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.