Exploring Targeted and Stealthy False Data Injection Attacks via Adversarial Machine Learning-Reference-Cited by-同舟云学术

Exploring Targeted and Stealthy False Data Injection Attacks via Adversarial Machine Learning

Author:

Tian Jiwei¹^ORCID,Wang Buhong²^ORCID,Li Jing³,Wang Zhen²^ORCID,Ma Bowen⁴,Ozay Mete⁵

Affiliation:

1. Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an, China

2. Information and Navigation College, Air Force Engineering University, Xi’an, China

3. School of Design and Art, Henan University of Technology, Zhengzhou, China

4. College of Cryptogram Engineering, Information Engineering University, Zhengzhou, China

5. Department of Electrical Engineering, Princeton University, Princeton, NJ, USA

Funder

National Natural Science Foundation of China

National University of Defense Technology Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

Reference54 articles.

1. Explaining and harnessing adversarial examples;goodfellow;arXiv 1412 6572,2014

2. On the properties of the softmax function with application in game theory and reinforcement learning;gao;arXiv 1704 00805,2017

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