109 citations · 339 across the 38 of their papers we have counts for
39 papers
AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents
Jiajun Zhou, Zhaoxuan Ke, Jihang Ye +3
Large language model (LLM)-based workspace agents execute stateful, multi-step workflows across heterogeneous resources, external tools, and persistent state. Their safety must the…
MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
Xuanze Chen, Xukang Xie, Wentao Fu +3
Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist. A malicious instruction crafted by an attacker…
Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification
Jiajun Zhou, Yadong Li, Xuanze Chen +4
Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static and rigid routing strategies…
CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
Xuanze Chen, Jiajun Zhou, Yadong Li +2
Heterogeneous graph representation learning (HGRL) is essential for modeling complex systems with diverse node and edge types. However, most existing methods are limited to closed-…
Cosmic anisotropic hair of nonlocal RT gravity
Jiajun Zhou, Shuxun Tian, Zong-Hong Zhu
Nonlocal RT gravity has proven effective in explaining the late-time cosmic acceleration while remaining consistent with local gravity tests. However, most previous cosmological st…
Traffic-MoE: A Sparse Foundation Model for Network Traffic Security Analysis
Jiajun Zhou, Changhui Sun, Wentao Fu +3
As adversaries increasingly weaponize encryption and protocol obfuscation to evade traffic detection, traditional methods are rendered obsolete, necessitating deep learning to unma…
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
Songtao Peng, Lei Wang, Wu Shuai +4
With the rapid growth of internet traffic, malicious network attacks have become increasingly frequent and sophisticated, posing significant threats to global cybersecurity. Tradit…
Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection
Junhao Wu, Yixin Yang, Chengxiang Jin +5
With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the sec…
Mixture of Message Passing Experts with Routing Entropy Regularization for Node Classification
Xuanze Chen, Jiajun Zhou, Yadong Li +3
Graph neural networks (GNNs) have achieved significant progress in graph-based learning tasks, yet their performance often deteriorates when facing heterophilous structures where c…
Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph
Jiajun Zhou, Wentao Fu, Hao Song +2
As the Internet rapidly expands, the increasing complexity and diversity of network activities pose significant challenges to effective network governance and security regulation.…
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
Xuanze Chen, Jiajun Zhou, Shanqing Yu +1
Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through s…
Lateral Movement Detection via Time-aware Subgraph Classification on Authentication Logs
Jiajun Zhou, Jiacheng Yao, Xuanze Chen +3
Lateral movement is a crucial component of advanced persistent threat (APT) attacks in networks. Attackers exploit security vulnerabilities in internal networks or IoT devices, exp…
Rethinking Graph Transformer Architecture Design for Node Classification
Jiajun Zhou, Xuanze Chen, Chenxuan Xie +3
Graph Transformer (GT), as a special type of Graph Neural Networks (GNNs), utilizes multi-head attention to facilitate high-order message passing. However, this also imposes severa…
Network Anomaly Traffic Detection via Multi-view Feature Fusion
Song Hao, Wentao Fu, Xuanze Chen +4
Traditional anomalous traffic detection methods are based on single-view analysis, which has obvious limitations in dealing with complex attacks and encrypted communications. In th…
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
Chenxiang Jin, Jiajun Zhou, Chenxuan Xie +3
The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations. However, multiple imbalanc…
Facilitating Feature and Topology Lightweighting: An Ethereum Transaction Graph Compression Method for Malicious Account Detection
Jiajun Zhou, Xuanze Chen, Shengbo Gong +4
Ethereum has become one of the primary global platforms for cryptocurrency, playing an important role in promoting the diversification of the financial ecosystem. However, the rela…
Dual-view Aware Smart Contract Vulnerability Detection for Ethereum
Jiacheng Yao, Maolin Wang, Wanqi Chen +4
The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse…
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
Lijin Wu, Shanshan Lei, Feilong Liao +5
As the number of IoT devices increases, security concerns become more prominent. The impact of threats can be minimized by deploying Network Intrusion Detection System (NIDS) by mo…
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
Hao Song, Jiacheng Yao, Zhengxi Li +6
Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sou…
Multi-triplet Feature Augmentation for Ponzi Scheme Detection in Ethereum
Chengxiang Jin, Jiajun Zhou, Shengbo Gong +2
Blockchain technology revolutionizes the Internet, but also poses increasing risks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing scams,…
PathMLP: Smooth Path Towards High-order Homophily
Jiajun Zhou, Chenxuan Xie, Shengbo Gong +4
Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph n…
Clarify Confused Nodes via Separated Learning
Jiajun Zhou, Shengbo Gong, Xuanze Chen +4
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, c…
Neighborhood Homophily-based Graph Convolutional Network
Shengbo Gong, Jiajun Zhou, Chenxuan Xie +1
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical…
Data Augmentation on Graphs: A Technical Survey
Jiajun Zhou, Chenxuan Xie, Shengbo Gong +4
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in com…