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Jielun Zhang

Has grant

Assistant Professor

North Dakota State University

United States

Research Interests

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Dr. Jielun Zhang is an Assistant Professor at the University of North Dakota, United States. His research spans cross-domain intrusion detection, malicious traffic synthesis, and AI-based network traffic classification. He has contributed to studies on network traffic clustering, wireless communication platforms for IoT applications, and intelligent network traffic prediction.

Recent Grants

Grant: Open

ERI: An Edge-Based Staleness-Resilient Data Synthesis Framework for Edge AI application in Network Measurement

Open Date: 2025-10-01

Close Date: 2027-09-30

Grant: Open

CRII: SaTC: Towards Sustainable Intrusion Detection for Edge via Spatial Collaboration and Temporal Knowledge Retention

Open Date: 2025-07-01

Close Date: 2027-06-30

Grant: Close

IUCRC Planning Grant University of North Dakota: Center for Infrastructure Security in the Era of AI (ISEAI)

Open Date: 2024-09-01

Close Date: 2025-08-31

Articles (17)

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2026

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2025

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2023

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2022

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2022

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2020

Collaborators (3)

Chris Farnell

Assistant Professor

University of Arkansas

UNITED STATES

View details

Feng Ye

Assistant Professor

University of Wisconsin-Madison

UNITED STATES

View details

Tanzim Jim Hassan

Assistant Professor

Dakota State University

UNITED STATES

View details

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Jielun Zhang

Research Interests

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About

Recent Grants

Articles (17)

A blockchain-enabled hybrid intrusion detection framework for resource-constrained devices

MEFA: Multisource Entropy‐Weighted Feature Adaptation for Cross‐Domain Intrusion Detection

A Comprehensive Study on Lightweight Convolution Techniques for Malicious Traffic Synthesis in Diffusion Models

Evaluating Model Resilience to Data Poisoning Attacks: A Comparative Study

Anomaly Detection in CPS using LSTM-Autoencoder with OCSVM on SWaT Dataset

Bayesian Deep Learning with Multi-Spectral Inputs for Secure Crop Health Monitoring

Blockchain-Enabled Distributed Intrusion Detection System for Securing IoT Networks

Edge-Based Collaborative Log Anomaly Detection Using Retrieval-Augmented Language Models

From Sensor Signals to Safety Statements: LLM-Based Justification Generation for Process Engineering

Model-Agnostic Unsupervised Detection of Prompt Injection with Multiscale Perplexity Signatures

Physical and Cyber Security Vulnerabilities in Substation Infrastructure

Towards Explainable Federated Intrusion Detection for IoT in Resource-Constrained Environments

Towards Prompt and Trustworthy SoH Monitoring for Safety-Critical Battery Systems

Sustaining the High Performance of AI-Based Network Traffic Classification Models

Network traffic clustering with QoS-awareness

Seamless Wireless Communication Platform for Internet of Things Applications

Intelligent and Application-Aware Network Traffic Prediction in Smart Access Gateways

Collaborators (3)

Chris Farnell

Feng Ye

Tanzim Jim Hassan

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