Shengjie Xu

Interests

Research

Cybersecurity, AI, Machine Learning, Zero Trust, Graph Modeling, Edge Computing, Edge AI, Cryptography

Teaching

Cybersecurity, AI, Machine Learning, Computer Networks

Courses

Scholarly Contributions

Books

• Xu, S., Qian, Y. i., & Hu, R. Q. (2022). Cybersecurity in Intelligent Networking Systems. John Wiley & Sons.

Chapters

• Xu, S., Qian, Y. i., & Hu, R. Q. (2023). Adversarial Examples: Challenges and Solutions. In Cybersecurity in Intelligent Networking Systems.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2023). Cyber Threats and Gateway Defense. In Cybersecurity in Intelligent Networking Systems.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2023). Cybersecurity in the Era of Artificial Intelligence. In Cybersecurity in Intelligent Networking Systems.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2023). Edge Computing and Secure Edge Intelligence. In Cybersecurity in Intelligent Networking Systems.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2023). Efficient Pre-processing Scheme for Anomaly Detection. In Cybersecurity in Intelligent Networking Systems.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2023). Privacy preservation in the era of big data. In Cybersecurity in Intelligent Networking Systems.
• Xu, S., Xu, S., Qian, Y. i., Qian, Y. i., Hu, R. Q., & Hu, R. Q. (2023). Robust Intrusion Detection. In Cybersecurity in Intelligent Networking Systems.

Journals/Publications

• McCully, G. A., Hastings, J. D., & Xu, S. (2024). Impact of Data Snooping on Deep Learning Models for Locating Vulnerabilities in Lifted Code. arXiv preprint arXiv:2412.02048.
• McCully, G. A., Hastings, J. D., Xu, S., & Fortier, A. (2024). Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code. arXiv preprint arXiv:2405.20611.
• McCully, G. A., Hastings, J. D., Xu, S., & Fortier, A. (2024). Comparing unidirectional, bidirectional, and word2vec models for discovering vulnerabilities in compiled lifted code. arXiv preprint arXiv:2409.17513.
• Gyawali, S., Xu, S., Qian, Y., & Hu, R. (2021). Challenges and Solutions for Cellular Based V2X Communications. IEEE Communications Surveys and Tutorials, 23(1). doi:10.1109/COMST.2020.3029723
  More info

  A wide variety of works have been conducted in vehicle-to-everything (V2X) communications to enable a variety of applications for road safety, traffic efficiency and passenger infotainment. Although dedicated short-range communications (DSRC) based V2X is already in the deployment phase, cellular based V2X is gaining more interest in academia and industry most recently. This article surveys the existing work and challenges on LTE and 5G to support efficient V2X communications. First, we present the motivations for cellular based V2X communications. Second, we summarize the LTE V2X architecture and operating scenarios being considered. Third, we discuss the challenges in existing LTE for supporting V2X communications such as physical layer structure, synchronization, multimedia broadcast multicast services (MBMS), resource allocation, security and survey the recent solutions to these challenges. We further discuss the challenges and possible solutions for 5G based vehicular communications. Finally, we discuss the open research issues and possible research directions in cellular based vehicular communications.
• Gyawali, S., Xu, S., Qian, Y. i., & Hu, R. Q. (2020). Challenges and solutions for cellular based V2X communications. IEEE Communications Surveys & Tutorials, 23(1), 222--255.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2020). Edge intelligence assisted gateway defense in cyber security. IEEE Network, 34(4), 14--19.
• Xu, S., Qian, Y., & Hu, R. (2020). Data-Driven Edge Intelligence for Robust Network Anomaly Detection. IEEE Transactions on Network Science and Engineering, 7(3). doi:10.1109/TNSE.2019.2936466
  More info

  The advancement of networking platforms for assured online services requires robust and effective network intelligence systems against anomalous events and malicious threats. With the rapid development of modern communication technologies, artificial intelligence, and the revolution of computing devices, cloud computing empowered network intelligence will inevitably become a core platform for various smart applications. While cloud computing provides strong and powerful computation, storage, and networking services to detect and defend cyber threats, edge computing on the other hand will deliver more benefits in specific yet potential critical areas. In this paper, we present a study on the data-driven edge intelligence for robust network anomaly detection. We first highlight the main motivations for edge intelligence, and then propose an intelligence system empowered by edge computing for network anomaly detection. We further propose a scheme on the data-driven robust network anomaly detection. In the proposed scheme, four phases are designed to incorporate with data-driven approaches to train a learning model which is able to detect and identify a network anomaly in a robust way. In the performance evaluations with data experiments, we demonstrate that the proposed scheme achieves the robustness of trained model and the efficiency on the detection of specific anomalies.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2019). Data-driven edge intelligence for robust network anomaly detection. IEEE Transactions on Network Science and Engineering, 7(3), 1481--1492.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2019). Data-driven network intelligence for anomaly detection. IEEE Network, 33(3), 88--95.
• Xu, S., & Qian, Y. i. (2018). Machine Learning Enabled Data Preprocessing in Cyber Security Applications. IEEE ComSoc MMTC Communications - Frontiers, 13(6), 15--18.
• Xu, S., Qian, Y. i., & Qingyang, H. R. (2018). Reliable and resilient access network design for advanced metering infrastructures in smart grid. IET Smart Grid, 1(1), 24--30.
• Xu, S., Qian, Y. i., & Hu, R. Q. (2015). On reliability of smart grid neighborhood area networks. IEEE Access, 3, 2352--2365.

Proceedings Publications

• Fehrman, B., Akowuah, F., & Xu, S. (2024). Adversarial Attacks in Problem Space for VBA Code Samples. In International Symposium on Intelligent Computing and Networking.
• Ikei, B., Thiry, H., & Xu, S. (2024). Towards Robust IoT Security: A Blockchain Design with Attribute-Based Encryption. In International Symposium on Intelligent Computing and Networking.
• Liu, J., Vyas, P., & Xu, S. (2024). Real-Time Fake News Detection on the X (Twitter): An Online Machine Learning Approach. In Americas' Conference on Information Systems (AMCIS).
• McCully, G., Hastings, J., Xu, S., & Fortier, A. (2024). Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code. In 2024 IEEE Cyber Awareness and Research Symposium (CARS).
• Nelson, T., O’Brien, A., Noteboom, C., & Xu, S. (2024). WCFG: A Weighted Control Flow Graph Dataset Design for Malware Classification. In International Symposium on Intelligent Computing and Networking.
• Zhang, K., Xu, S., & Shin, B. (2023). Towards Adaptive Zero Trust Model for Secure AI. In 2023 IEEE Conference on Communications and Network Security (CNS).
• Ahmed, T., & Xu, S. (2022). Shellcoding: Hunting for Kernel32 Base Address. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
• Bradley, M., & Xu, S. (2021). A Metric for Machine Learning Vulnerability to Adversarial Examples. In IEEE International Conference on Computer Communications (INFOCOM).
• Burr, J., & Xu, S. (2021). Improving Adversarial Attacks Against Executable Raw Byte Classifiers. In IEEE International Conference on Computer Communications (INFOCOM).
• Chaudhary, S., O’Brien, A., & Xu, S. (2020). Automated post-breach penetration testing through reinforcement learning. In 2020 IEEE Conference on Communications and Network Security (CNS).
• Fang, D., Xu, S., & Sharif, H. (2019). Security analysis of wireless train control systems. In 2019 IEEE Globecom Workshops (GC Wkshps).
• Xu, S., Fang, D., & Sharif, H. (2019). Efficient network anomaly detection for edge gateway defense in 5G. In 2019 IEEE Globecom Workshops (GC Wkshps).
• Xu, S., Qian, Y. i., & Hu, R. Q. (2019). A semi-supervised learning approach for network anomaly detection in fog computing. In ICC 2019-2019 IEEE International Conference on Communications (ICC).
• Gyawali, S., Xu, S., Ye, F., Hu, R. Q., & Qian, Y. i. (2018). A D2D based clustering scheme for public safety communications. In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).
• Xu, S., & Ye, F. (2018). A predicate encryption based anomaly detection scheme for e-Health communications network. In 2018 IEEE International Conference on Communications (ICC).
• Xu, S., Qian, Y. i., & Hu, R. Q. (2018). Privacy-preserving data preprocessing for fog computing in 5G network security. In 2018 IEEE Global Communications Conference (GLOBECOM).
• Xu, S., Qian, Y. i., & Hu, R. Q. (2017). A data-driven preprocessing scheme on anomaly detection in big data applications. In 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
• Xu, S., Qian, Y. i., & Hu, R. Q. (2017). A study on communication network reliability for advanced metering infrastructure in smart grid. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech).
• Xu, S., Qian, Y. i., & Hu, R. Q. (2016). A secure data learning scheme in big data applications. In 2016 25th International Conference on Computer Communication and Networks (ICCCN).
• Xu, S., & Qian, Y. i. (2015). Quantitative study of reliable communication infrastructure in smart grid NAN. In 2015 11th International Conference on the Design of Reliable Communication Networks (DRCN).
• Xu, S., Lei, X., Li, J., & Shi, H. (2011). USB Storage Device Surveillance System in Intranet. In Proceedings of the 2011 International Conference on Information Security and Intelligence Control.

Others

• Zurawski, J. (2021, November). South Dakota Region Scientific Deep Dive. osti.gov.