2 citations · 6 across the 8 of their papers we have counts for
9 papers
DiagChain: A Diagnostic Benchmark for Evaluating LLM Agents on Evidence-Grounded Attack Chain Reconstruction
Xuyang Liu, Yibin Han, Zhenwei Zhang +8
Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to infer ordered attacker actions…
Semantic Code Clone Detection: Are We There Yet?
Zhiwei Xu, Weixian Deng, Xuyang Liu +5
Code clone detection has been extensively studied for decades, and recent approaches have begun reporting remarkably high performance for semantic (Type-4) clones on benchmark data…
Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning
Haoran Dang, Cuiling Lan, Hai Wan +2
Temperature is a crucial hyperparameter in large language models (LLMs), controlling the trade-off between exploration and exploitation during text generation. High temperatures en…
Adversarial Contrastive Learning for LLM Quantization Attacks
Dinghong Song, Zhiwei Xu, Hai Wan +3
Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe security risks that benign LLMs in f…
Ancora: Accurate Intrusion Recovery for Web Applications
Yihao Peng, Biao Ma, Hai Wan +1
Modern web application recovery presents a critical dilemma. Coarse-grained snapshot rollbacks cause unacceptable data loss for legitimate users. Surgically removing an attack's im…
Deep Learning-based Intrusion Detection Systems: A Survey
Zhiwei Xu, Yujuan Wu, Shiheng Wang +5
Intrusion Detection Systems (IDS) have long been a hot topic in the cybersecurity community. In recent years, with the introduction of deep learning (DL) techniques, IDS have made…
LESS: Efficient Log Storage System Based on Learned Model and Minimum Attribute Tree
Zhiyang Cheng, Zizhen Zhu, Haoran Dang +2
In recent years, cyber attacks have become increasingly sophisticated and persistent. Detection and investigation based on the provenance graph can effectively mitigate cyber intru…
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
Fan Xu, Nan Wang, Hao Wu +7
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to…
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
Fan Xu, Nan Wang, Hao Wu +3
Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely ap…