Zhuo Lu
Cited by
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Cyber security in the smart grid: Survey and challenges
W Wang, Z Lu
Computer networks 57 (5), 1344-1371, 2013
Review and evaluation of security threats on the communication networks in the smart grid
Z Lu, X Lu, W Wang, C Wang
2010-Milcom 2010 Military Communications Conference, 1830-1835, 2010
Modeling, evaluation and detection of jamming attacks in time-critical wireless applications
Z Lu, W Wang, C Wang
IEEE Transactions on Mobile Computing 13 (8), 1746-1759, 2013
Adversarial deep learning for cognitive radio security: Jamming attack and defense strategies
Y Shi, YE Sagduyu, T Erpek, K Davaslioglu, Z Lu, JH Li
2018 IEEE international conference on communications workshops (ICC …, 2018
Secure edge computing in IoT systems: Review and case studies
M Alrowaily, Z Lu
2018 IEEE/ACM symposium on edge computing (SEC), 440-444, 2018
Cyber deception: Overview and the road ahead
C Wang, Z Lu
IEEE Security & Privacy 16 (2), 80-85, 2018
From jammer to gambler: Modeling and detection of jamming attacks against time-critical traffic
Z Lu, W Wang, C Wang
2011 Proceedings IEEE INFOCOM, 1871-1879, 2011
On network performance evaluation toward the smart grid: A case study of DNP3 over TCP/IP
X Lu, Z Lu, W Wang, J Ma
2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, 1-6, 2011
Deep learning-aided cyber-attack detection in power transmission systems
D Wilson, Y Tang, J Yan, Z Lu
2018 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2018
Contextual combinatorial multi-armed bandits with volatile arms and submodular reward
L Chen, J Xu, Z Lu
Advances in Neural Information Processing Systems 31, 2018
Generalized federated learning via sharpness aware minimization
Z Qu, X Li, R Duan, Y Liu, B Tang, Z Lu
International Conference on Machine Learning, 18250-18280, 2022
When attackers meet AI: Learning-empowered attacks in cooperative spectrum sensing
Z Luo, S Zhao, Z Lu, J Xu, YE Sagduyu
IEEE Transactions on Mobile Computing 21 (5), 1892-1908, 2020
Lomar: A local defense against poisoning attack on federated learning
X Li, Z Qu, S Zhao, B Tang, Z Lu, Y Liu
IEEE Transactions on Dependable and Secure Computing, 2021
Adversarial machine learning based partial-model attack in IoT
Z Luo, S Zhao, Z Lu, YE Sagduyu, J Xu
Proceedings of the 2nd ACM workshop on wireless security and machine …, 2020
When wireless security meets machine learning: Motivation, challenges, and research directions
YE Sagduyu, Y Shi, T Erpek, W Headley, B Flowers, G Stantchev, Z Lu
arXiv preprint arXiv:2001.08883, 2020
Camouflage traffic: Minimizing message delay for smart grid applications under jamming
Z Lu, W Wang, C Wang
IEEE Transactions on Dependable and Secure Computing 12 (1), 31-44, 2014
Effectiveness of machine learning based intrusion detection systems
M Alrowaily, F Alenezi, Z Lu
Security, Privacy, and Anonymity in Computation, Communication, and Storage …, 2019
No training hurdles: Fast training-agnostic attacks to infer your typing
S Fang, I Markwood, Y Liu, S Zhao, Z Lu, H Zhu
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018
On the evolution and impact of mobile botnets in wireless networks
Z Lu, W Wang, C Wang
IEEE Transactions on Mobile Computing 15 (9), 2304-2316, 2015
Context-aware online client selection for hierarchical federated learning
Z Qu, R Duan, L Chen, J Xu, Z Lu, Y Liu
IEEE Transactions on Parallel and Distributed Systems 33 (12), 4353-4367, 2022
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