From Code to EM Signals: A Generative Approach to Side Channel Analysis-based Anomaly Detection

Author:

Vedros Kurt A.1ORCID,Kolias Constantinos1ORCID,Barbara Daniel2ORCID,Ivans Robert C.3ORCID

Affiliation:

1. University of Idaho, USA

2. George Mason University, USA

3. Idaho National Laboratory, USA

Funder

Department of Energy

Publisher

ACM

Reference20 articles.

1. Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In International conference on machine learning. PMLR, 214–223.

2. Generative adversarial networks

3. Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017. Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Vol. 30. Curran Associates, Inc.

4. Deep Residual Learning for Image Recognition

5. Haider Adnan Khan, Monjur Alam, Alenka Zajic, and Milos Prvulovic. 2018. Detailed tracking of program control flow using analog side-channel signals: a promise for iot malware detection and a threat for many cryptographic implementations. In Cyber Sensing 2018, Vol. 10630. International Society for Optics and Photonics, 1063005.

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