Zhida Li received the B.E. and M.A.Sc. degrees in Electrical Engineering and Microelectronic Design from the University College Cork, Ireland, in 2011 and 2015, respectively. He was a research assistant at Tyndall National Institute, Cork, Ireland, from 2011 to 2014. He is currently working toward the Ph.D. degree in Simon Fraser University from Fall 2015. His current research project is related to machine learning techniques for classifying network anomalies and intrusions.
He serves as Secretary (2019-2020) of the IEEE SFU Student Branch.
Zhida Li was the TA for ENSC 252 (Fundamentals of Digital Logic and Design) in Fall 2015 and Summer 2016,
ENSC 180 (Introduction to Engineering Analysis) in Spring 2017, ENSC 220 (Electric Circuit I) in Fall 2016 and
Fall 2017, and ENSC 427/835 (Communication Networks) in Spring 2018.
He is the IEEE student member.
Supervisor: Prof. Ljiljana Trajkovic
- Analytics of social networks
- Machine learning techniques for classifying network anomalies and intrusions
Detecting, analyzing, and defending against cyber threats is an important topic in cyber security. A variety of machine learning models have been designed to help detect malicious intentions of network users. We employ two deep learning Recurrent Neural Networks (RNNs) with a variable number of hidden layers: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). An alternative to deep learning networks is the recently proposed Broad Learning System (BLS). We evaluate the original BLS and its extensions that employ radial basis function (RBF) and cascades of mapped features and enhancement nodes. The models are trained and tested using Border Gateway Protocol (BGP) datasets that contain routing records collected from Reseaux IP Europeens (RIPE) and BCNET and the NLS-KDD dataset. The algorithms are compared based on accuracy and F-Score.
- Deep learning algorithms
Z. Li, A. L. Gonzalez Rios, G. Xu, and Lj. Trajkovic,
"Machine learning techniques for classifying network anomalies and intrusions,"
in Proc. IEEE Int. Symp. Circuits and Systems, Sapporo, Japan, May 2019, pp. 1-4.
A. L. Gonzalez Rios, Z. Li, G. Xu, A. Diaz Alonso, and Lj. Trajkovic,
"Detecting network anomalies and intrusions in communication networks,"
in Proc. 23rd IEEE International Conference on Intelligent Engineering Systems 2019,
Godollo, Hungary, April 2019, pp. 29-34.
Z. Li, P. Batta, and Lj. Trajkovic,
of machine learning algorithms for detection of network intrusions,"
IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018),
Miyazaki, Japan, Oct. 2018, pp. 4238-4243.
Q. Ding, Z. Li, S. Haeri, and Lj. Trajkovic,
of machine learning techniques to detecting anomalies in communication networks: datasets and feature selection algorithms,"
in Cyber Threat Intelligence, M. Conti, A. Dehghantanha, and T. Dargahi, Eds., Berlin: Springer, pp. 47-70, 2018.
Z. Li, Q. Ding, S. Haeri, and Lj. Trajkovic,
of machine learning techniques to detecting anomalies in communication networks: classification algorithms,"
in Cyber Threat Intelligence, M. Conti, A. Dehghantanha, and T. Dargahi, Eds., Berlin: Springer, pp. 71-92, 2018.
P. Batta, M. Singh, Z. Li, Q. Ding, and Lj. Trajkovic,
"Evaluation of support vector machine kernels for detecting network anomalies,"
IEEE Int. Symp. Circuits and Systems, Florence, Italy, May 2018, pp. 1-4.
H. Ben Yedder, Q. Ding, U. Zakia, Z. Li, S. Haeri, and Lj. Trajkovic,
virtualization algorithms and topologies for data center networks,"
The 26th International Conference on Computer Communications and Networks (ICCCN 2017),
2nd Workshop on Network Security Analytics and Automation (NSAA),
Vancouver, Canada, Aug. 2017.
Q. Ding, Z. Li, P. Batta, and Lj. Trajkovic,
"Detecting BGP anomalies using machine learning techniques,"
in Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016),
Budapest, Hungary, Oct. 2016, pp. 3352-3355.
S. Haeri, Q. Ding, Z. Li, and Lj. Trajkovic,
resource capacity algorithm with path splitting for virtual network embedding,"
in Proc. IEEE Int. Symp. Circuits and Systems,
Montreal, Canada, May 2016, pp. 666-669.
M.P. Kennedy, H. Mo, Z. Li, G. Hu, P. Scognamiglio, and E. Napoli,
"The Noise and Spur Delusion in Fractional-N Frequency Synthesizer Design,"
The IEEE International Symposium on Circuits and Systems (ISCAS),
Lisbon, Portugal, 24-27 May 2015.
Z. Li, H. Mo, and M.P. Kennedy,
"Comparative Spur Performance of a Fractional-N Frequency Synthesizer
with a Nested MASH-SQ3 Divider Controller in the Presence of Memoryless Piecewise-Linear and Polynomial Nonlinearities,"
in Proc. ISSC 2014,
Limerick, 26-27 June 2014, pp. 1-6.
M.P. Kennedy, Z. Li, and H. Mo,
"How to Eliminate Integer Boundary Spurs in Fractional-N Frequency Synthesizers?,"
in Proc. Communication and Radio Sciences into the 21st Century,
Dublin, 30 April-01 May 2014, pp. 1-4.
M.P. Kennedy, Z. Li, and Z. Huang,
"Programmable analog frequency divider based on p-switching,"
Nonlinear Theory and Its Applications, IEICE,
4(4): 389-399, 01 October 2013.
Z. Li and M.P. Kennedy,
"The Switched Injection-Locked Oscillator (SILO) Concept,"
in Proc. Nonlinear Theory and Its Applications (NOLTA) 2012,
Palma, Mallorca, 22-26 October 2012, pp. 868-871.
Detection of network anomalies
Last updated on January 04, 2020.