SINR-Based DoS Attack on Remote State Estimation: A Game-Theoretic Approach

By Yuzhe Li, Daniel E. Quevedo, Subhrakanti Dey, Ling Shi. IEEE Transactions on Control of Network Systems, 2017

297 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/57d0a9eca5a0a94d9e1c1e640bf8b54aaaac9b75

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Hongtao Sun
2. Syed Ali Hassan
3. Ekram Hossain
4. Chen Peng
5. Rasheed Hussain
6. Liang Xiao
7. Yanyong Zhang
8. Di Wu

In Control and Systems Engineering

1. Hongtao Sun
2. Ekram Hossain
3. Chen Peng
4. Yulong Wang
5. Mingjin Yang
6. Rasheed Hussain
7. Qing-Long Han
8. Xianming Zhang

Citations in the corpus

• Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “Distributed DOS Attack • K-Nearest Neighbour [75] • Support Vector Machine [75] • Random Forest and Decision Tree [75] • Neural Network [75] • Multivariate Correlation Analysis (MCA) [92] • Q learning [93]”
• A Survey on Security Communication and Control for Smart Grids Under Malicious Cyber Attacks (2019, background): “In order to characterize the dynamics of the attack sides and defense sides, game theory framework supplies an effective way to model this interactive decision-making process between the two sides as well as FDI attacks [37], [38].”
• IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security? (2018, methodology): “For example, Q-learning as a model-free RL technique has been used to improve the performance of authentication [8], antijamming offloading [10], [19], [20], and malware detection [11], [21].”
• Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, unknown): “In [49], [51], [137]-[138], Q-learning has been”