Biography

Zhida Li received the B.E. and M.Eng.Sc. degrees in electrical engineering and microelectronic design from the University College Cork, Ireland, respectively. He received his Ph.D. degree in engineering science from Simon Fraser University (SFU), Canada, under the supervision of Prof. Ljiljana Trajković.

He is currently an Assistant Professor in the Department of Computer Science at New York Institute of Technology - Vancouver Campus, British Columbia, Canada. From 2011 to 2014, he was a Research Assistant at Tyndall National Institute, Ireland. He was a Postdoctoral Fellow at SFU, from June 2022 to Dec. 2022. His research interests include machine learning systems for detecting network anomalies, brain-computer interfaces, and blockchain.

Dr. Li serves as Secretary (2022-present) of the Membership Development Committee, IEEE Canada. He is the Secretary (2024–Present) of IEEE Vancouver Section, the Chair (2023-present) of the IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections, and the Counselor (2023–Present) of New York Tech-Vancouver. He served as a Technical Program Committee (TPC) Member of the IEEE International Conference on High Performance Computing and Communications (HPCC) 2020 and a Session Chair at IEEE International Conference on Cyber, Physical, and Social Computing (CPSCom) 2020. He served as a Publicity Chair and a TPC member of the CPSCom 2022 and a TPC member of the International Conference on E-Business and Internet (ICEBI) 2023. He is a TPC member of the International Conference on Intelligent Sensing and Industrial Automation (ISIA) 2023. He is a Member of the IEEE.

Teaching

Spring 2024 Fall 2023 Summer 2023 Spring 2023

Events

Research Interests

  1. Applications of Machine Learning Techniques for Classifying Network Anomalies
    • Processed the raw network data: Border Gateway Protocol (BGP) data from RIPE and Route Views
    • Analyzed and implement various machine learning algorithms as well as employing feature selection
    • Performed experiments using a supercomputer managed by Compute Canada
  2. Development of Novel Machine Learning Algorithms: VFBLS and VCFBLS
    • Developed two fast BLS-based algorithms: variable features BLS algorithms without (VFBLS) and with cascades (VCFBLS)
    • Developed generalized models based on various subsets of input data based on selected features and expanded the network structure
  3. Development of Tool for Detecting Network Anomalies: BGPGuard
    • Developed BGPGuard tool that consists of multiple modules: real-time data retrieval, feature extraction, label refinement, data partition, data processing, ML algorithms, parameter selection, ML models, and classification
    • Developed a web-based version for real-time anomaly detection and off-line classification
  4. Brain-Computer Interface:
    • Analyze electroencephalogram (EEG) benchmarks
    • Develop new algorithms and approaches to analyze data from non-invasive collection of brain signals
  5. Blockchain:
    • Ethereum phishing detection: based on transaction records and labels collected from Etherscan
    • Develop graph neural networks to identify suspicious Ethereum accounts

Publications

2023
  • Z. Li and Lj. Trajković, "Enhancing Cyber Defense: Using Machine Learning Algorithms for Detection of Network Anomalies," in Proc. IEEE Int. Conf. Syst., Man, Cybern., Honolulu, USA, Oct. 2023, pp. 1658-1663.
  • T. Sharma, K. Patni, Z. Li, and Lj. Trajković, " Deep Echo State Networks for Detecting Internet Worm and Ransomware Attacks," in Proc. IEEE Int. Symp. Circuits Syst., Monterey, USA, May 2023, pp. 1-5.
  • Z. Li, A. L. G. Rios, and Lj. Trajković, " Machine learning for detecting the WestRock ransomware attack using BGP routing records," IEEE Communications Magazine, vol. 61, no. 3, pp. 20-26, Mar. 2023. (IF: 11.2)
    • 2021
  • Z. Li, A. L. G. Rios, and Lj. Trajković, " Classifying denial of service attacks using fast machine learning algorithms," in Proc. IEEE Int. Conf. Syst., Man, Cybern., Melbourne, Australia, Oct. 2021, pp. 1221-1226 (virtual).
  • Z. Li, A. L. G. Rios, and Lj. Trajković, " Machine learning for detecting anomalies and intrusions in communication networks," IEEE Journal on Selected Areas in Communications (JSAC), vol. 39, no. 7, pp. 2254-2264, July 2021. (IF: 16.4)
    • 2020
  • Z. Li, A. L. G. Rios, and Lj. Trajković, "Detecting Internet worms, ransomware, and blackouts using recurrent neural networks," in Proc. IEEE Int. Conf. Syst., Man, Cybern., Toronto, Canada, Oct. 2020, pp. 2165-2172 (virtual).
  • A. L. G. Rios, Z. Li, K. Bekshentayeva, and Lj. Trajković, " Detection of denial of service attacks in communication networks," in Proc. IEEE Int. Symp. Circuits Syst., Seville, Spain, Oct. 2020 (virtual).
    • 2019
  • Z. Li, A. L. G. Rios, G. Xu, and Lj. Trajković, " Machine learning techniques for classifying network anomalies and intrusions," in Proc. IEEE Int. Symp. Circuits Syst., Sapporo, Japan, May 2019, pp. 1-5.
  • A. L. G. Rios, Z. Li, G. Xu, A. D. Alonso, and Lj. Trajković, " Detecting network anomalies and intrusions in communication networks," in Proc. 23rd IEEE Int. Conf. Intell. Eng. Syst., Godollo, Hungary, Apr. 2019, pp. 29-34.
    • 2018
  • Z. Li, P. Batta, and Lj. Trajković, " Comparison of machine learning algorithms for detection of network intrusions," in Proc. IEEE Int. Conf. Syst., Man, Cybern., Miyazaki, Japan, Oct. 2018, pp. 4248-4253.
  • Q. Ding, Z. Li, S. Haeri, and Lj. Trajković, " Application of machine learning techniques to detecting anomalies in communication networks: datasets and feature selection algorithms," in Cyber Threat Intelligence, A. Dehghantanha, M. Conti, and T. Dargahi, Eds., Berlin: Springer, pp. 47-70, 2018.
  • Z. Li, Q. Ding, S. Haeri, and Lj. Trajković, " Application of machine learning techniques to detecting anomalies in communication networks: classification algorithms," in Cyber Threat Intelligence, A. Dehghantanha, M. Conti, and T. Dargahi, Eds., Berlin: Springer, pp. 71-92, 2018.
  • P. Batta, M. Singh, Z. Li, Q. Ding, and Lj. Trajković, " Evaluation of support vector machine kernels for detecting network anomalies," in Proc. IEEE Int. Symp. Circuits Syst., Florence, Italy, May 2018, pp. 1-4.
    • 2017
  • H. B. Yedder, Q. Ding, U. Zakia, Z. Li, S. Haeri, and Lj. Trajković, " Comparison of virtualization algorithms and topologies for data center networks," in Proc. 26th Int. Conf. Comput. Commun. Netw., 2nd Workshop Netw. Security. Analytics Autom., Vancouver, Canada, Aug. 2017.
    • 2016
  • Q. Ding, Z. Li, P. Batta, and Lj. Trajković, " Detecting BGP anomalies using machine learning techniques," in Proc. IEEE Int. Conf. Syst., Man, Cybern., Budapest, Hungary, Oct. 2016, pp. 3352-3355.
  • S. Haeri, Q. Ding, Z. Li, and Lj. Trajković, " Global resource capacity algorithm with path splitting for virtual network embedding," in Proc. IEEE Int. Symp. Circuits Syst., Montreal, Canada, May 2016, pp. 666-669.
    • 2015
  • 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," in Proc. IEEE Int. Symp. Circuits Syst., Lisbon, Portugal, May 2015.
    • 2014
  • 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. 25th IET Irish Signals Syst. Conf., Limerick, Ireland, June 2014, pp. 374–379.
  • M.P. Kennedy, Z. Li, and H. Mo, "How to Eliminate Integer Boundary Spurs in Fractional-N Frequency Synthesizers," in Proc. RIA/URSI Research Colloquium Commun. Radio Sci. into the 21st Century, Dublin, Ireland, May 2014, pp. 1-4.
    • 2013
  • M.P. Kennedy, Z. Li, and Z. Huang, " Programmable analog frequency divider based on p-switching," Nonlinear Theory and Its Applications, IEICE, vol. 4, no. 4, pp. 389–399, Oct. 2013.
    • 2012
  • Z. Li and M.P. Kennedy, " The Switched Injection-Locked Oscillator (SILO) Concept," in Proc. Int. Symp. Nonlinear Theory and Its Applications (NOLTA), Palma, Mallorca, Oct. 2012, pp. 868-871.

    • Talks

    UNM ME Seminar Series 2022
  • Zhida Li, "Machine Learning for Classifying Anomalies and Intrusions in Communication Networks," Mechanical Engineering Seminar Series (UNM ME Seminar Series), University of New Mexico, Albuquerque, USA, Dec. 2, 2022 (virtual).

    • Recent Work

    Real-Time Network Anomaly Detection

    Network Topology Analysis

    Last updated on Mon 1 Apr 2024 03:24:07 PDT.

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