activity
20202025
most citedthreaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning

222 citations · 526 across the 6 of their papers we have counts for

collaborators

6 papers

cs.NI2025

6Vision: Image-encoding-based IPv6 Target Generation in Few-seed Scenarios

W. Zhang, G. Song, L. He +5

Efficient global Internet scanning is crucial for network measurement and security analysis. While existing target generation algorithms demonstrate remarkable performance in large…

cs.NI2024

Centralized Network Utility Maximization with Accelerated Gradient Method

Ying Tian, Zhiliang Wang, Xia Yin +3

Network utility maximization (NUM) is a well-studied problem for network traffic management and resource allocation. Because of the inherent decentralization and complexity of netw…

cs.NI2022

Your Router is My Prober: Measuring IPv6 Networks via ICMP Rate Limiting Side Channels

Long Pan, Jiahai Yang, Lin He +4

Active Internet measurements face challenges when some measurements require many remote vantage points. In this paper, we propose a novel technique for measuring remote IPv6 networ…

cs.CR2021★ 222 cited

threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning

Su Wang, Zhiliang Wang, Tao Zhou +6

Host-based threats such as Program Attack, Malware Implantation, and Advanced Persistent Threats (APT), are commonly adopted by modern attackers. Recent studies propose leveraging…

cs.CR2021★ 113 cited

DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

Dongqi Han, Zhiliang Wang, Wenqi Chen +6

Unsupervised Deep Learning (DL) techniques have been widely used in various security-related anomaly detection applications, owing to the great promise of being able to detect unfo…

cs.CR2020★ 191 cited

Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors

Dongqi Han, Zhiliang Wang, Ying Zhong +5

Machine learning (ML), especially deep learning (DL) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has shown to…