Xuezhi Wen
YOU?
Author Swipe
View article: Vehicle Detection Based on Improved Yolov11 and Attention Mechanism
Vehicle Detection Based on Improved Yolov11 and Attention Mechanism Open
n this paper, an improved vehicle detection method based on YOLOv11 and an attention mechanism is proposed. By optimizing the network structure and integrating the attention mechanism, the accuracy and efficiency of vehicle detection are s…
View article: PCB Defect Detection using Deep Learning and Synthetic Data Generation with ControlNet
PCB Defect Detection using Deep Learning and Synthetic Data Generation with ControlNet Open
This defect detection in printed circuit boards (PCBs) is crucial to ensure reliability and functionality of the equipment used in all Industries. Based on the analysis, this paper develops a new PCB defect detector composed of Swin Transf…
View article: Adversarial-Resistant Cloud Security Using Deep Learning-Enhanced Ensemble Hidden Markov Models
Adversarial-Resistant Cloud Security Using Deep Learning-Enhanced Ensemble Hidden Markov Models Open
View article: Improving Security-Sensitive Deep Learning Models through Adversarial Training and Hybrid Defense Mechanisms
Improving Security-Sensitive Deep Learning Models through Adversarial Training and Hybrid Defense Mechanisms Open
View article: Hybrid Ba*-Dwa Method for Cooperative Multi-Robot Path Planning
Hybrid Ba*-Dwa Method for Cooperative Multi-Robot Path Planning Open
View article: GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection Open
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 learn global features within the graphs. Moreover, most con…
View article: Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum Open
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 applied to GFD, characterizing the anomalous possibility of a…
View article: Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum Open
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 applied to GFD, characterizing the anomalous possibility of a…
View article: Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection Open
Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network…
View article: TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph
TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph Open
APT detection is difficult to detect due to the long-term latency, covert and slow multistage attack patterns of Advanced Persistent Threat (APT). To tackle these issues, we propose TBDetector, a transformer-based advanced persistent threa…
View article: A study On : Confidentiality Approach to Prevent Features Disclosure in IoT Situations
A study On : Confidentiality Approach to Prevent Features Disclosure in IoT Situations Open
This paper proposes an approach which safeguards confidentiality to avoid disclosures of features within a multiple IoT situation, that is, a setup of objects in networks that communicate with each other. Two ideas derived from the theory …