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 node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into various mixed-frequency bands based on the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors. We release our code at https://github.com/Sunxkissed/SEC-GFD.

Network attacks aim to exploit vulnerabilities inherent in network protocols, which is widely used in many real-world applications. In the process of network anomaly detection, most methods train the model by minimizing the average empirical risk of all samples. However, due to the uneven distribution of samples from different protocols, detection models tend to be biased against minority protocols groups. To address this issue, we propose an adaptive weight assignment method for network attack detection, which emphasizes more on error-prone samples in prediction and enhances adequate representation of minority groups for fairness. We conduct experiments on two widely used datasets, KDD and NSLKDD. According to the results, our method achieves better performance than state-of-the-art methods for classification and regression tasks, and is robust to label noise in the test dataset.

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 intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.

The focus of this paper is on the problem of Haar-like feature selection and classification for vehicle detection. Haar-like features are particularly attractive for vehicle detection because they form a compact representation, encode edge and structural information, capture information from multiple scales, and especially can be computed efficiently. Due to the large-scale nature of the Haar-like feature pool, we present a rapid and effective feature selection method via AdaBoost by combining a sample's feature value with its class label. Our approach is analyzed theoretically and empirically to show its efficiency. Then, an improved normalization algorithm for the selected feature values is designed to reduce the intra-class difference, while increasing the inter-class variability. Experimental results demonstrate that the proposed approaches not only speed up the feature selection process with AdaBoost, but also yield better detection performance than the state-of-the-art methods.

A rear-vehicle detection system of static images based on monocular vision is presented. It does not need the road boundary and lane information. Firstly, it segments the region of interest (ROI) by using the shadow underneath the vehicle and edges. Secondly, it accurately localizes the ROI by vehicle features such as symmetry, edges and the shadow underneath the vehicle, etc. Finally, it completes vehicle detection by combining knowledge-based and statistics-based methods. Under various illuminations and different roads (different day time, different scenes: highway, urban common road, urban narrow road), the system shows good results of recognition and performance