Transaction fraud detection (TFD) remains a substantial challenge in the digital economy. Feature extraction is crucial in TFD, as relying solely on raw transactional features fails to capture the complex and dynamic nature of fraudulent behavior. Existing methods primarily rely on manually aggregated features, failing to account for the evolving nature of fraud patterns. These limitations undermine the accuracy and adaptability of TFD systems. In this work, we propose a behavioral drift-aware feature extraction (BDFE) method designed to capture evolving fraud patterns by modeling behavioral drift and extracting new feature representations for TFD. BDFE consists of four synergistic modules. The first module is a feature extractor that encodes user transactional behaviors into new representations. The second and third modules are a historical behavior-aware classifier and a current behavior-aware classifier, which jointly guide the extractor to learn more discriminative features by capturing patterns from historical and current transactions, respectively. The fourth module is a behavior discriminator that distinguishes between historical and current behaviors. Through adversarial training, the discriminator encourages the feature extractor to extract unified representations that are invariant to behavioral shifts. This design enables BDFE to learn drift-resilient features, thus enhancing the detection of evolving fraud patterns. Extensive experiments are conducted on real-world transaction datasets and public ones. The results show that BDFE achieves improvements in average precision from 2.32% to 12.17%, in recall from 3.65% to 14.57%, in F1-score from 1.80% to 11.79%, and in G-mean from 2.05% to 8.17% over its peers. These gains demonstrate its superior feature extraction capability and its effectiveness in enhancing TFD performance.

With the exponential growth of network computing demands, service function chain (SFC) scaling can meet the evolving demands and provide more service functionalities, which is crucial for addressing the shortcomings of network resources. However, in multidomain and heterogeneous edge networks, existing SFC scaling methods aim to reduce the additional costs of delays and resources during scaling, which ignore the resource redundancy and accumulation caused by the high and low pressure of virtual network load. Additionally, the inappropriateness of selection order of interdomain transit nodes and intradomain service nodes leads to frequent SFC scaling and placement. To tackle these challenges, we propose a relative-cost-aware SFC collaborative scaling and placement (SFC-CSP) mechanism based on multiagent reinforcement learning (MARL) with Stackelberg game. First, we introduce a priority-based virtual network functions scaling queue to reduce the times of frequent SFC scaling. Then, to alleviate the imbalance between delay, resource redundancy, and resource accumulation, we establish a relative cost-based multiobjective optimization function. The aim is to minimize the delay cost, the relative computing resource cost, the storage resource cost, and bandwidth resource cost. Furthermore, to reduce the impact of selection order for interdomain transit node and intradomain service node on asynchronous SFC placement, we design an SFC-CSP mechanism based on MARL with Stackelberg game, considering the interaction between node action selections. Experimental results demonstrate that our proposed method not only achieves higher SFC acceptance rates compared with other methods but also performs well in reducing end-to-end delay of SFC and resource accumulation.

With higher peak data rates, enhanced reliability, improved energy efficiency, and reduced radio latency, 6G enables cooperative perception in autonomous vehicle clusters. Existing research mainly focuses on establishing communication-connected and structurally stable clusters, while overlooking how members collaborate in perception. To address this gap, we propose a collaborative perception-based autonomous vehicle cluster modeling method for expressways, leveraging the capabilities of 6G networks. This method facilitates collaborative perception within vehicle clusters through the exchange of sensory information among member vehicles. First, we introduce a perception interaction mechanism among vehicles as a foundation for constructing clusters. We then present a primary vehicle selection method and analyze the cluster’s perception gain, collaborative efficiency, and collaborative reliability. Based on this, we develop a vehicle cluster model and solve it using a genetic algorithm. We present a vehicle cluster formation method that brings together vehicles achieving Pareto optimal solutions through specific interaction rules, thereby forming a cluster. The simulation results demonstrate that the proposed method outperforms existing methods in terms of perception gain (PG), collaborative efficiency (CE), and collaborative reliability (CR).

In the digital era, effective Transaction Fraud Detection (TFD) is essential to ensuring financial security. The considerable class imbalance, with legitimate transactions vastly outnumbering fraudulent ones, presents a significant challenge for TFD models to accurately identify fraudulent patterns. While existing sample-balancing strategies address class imbalance effectively in many contexts, they often fall short in TFD due to fraudsters’ sophisticated concealment tactics, which lead to pronounced behavioral overlap between fraudulent and legitimate transactions. In this paper, we introduce a novel Generative Adversarial Network-based Hybrid Sampling method (GANHS) to effectively address the class imbalance issue. GANHS employs a dual-discriminator generative adversarial network to generate synthetic samples that accurately reflect the characteristics of fraudulent activity, while an adaptive neighborhood-based undersampling technique refines these samples to minimize overlap with legitimate ones. This hybrid approach not only enhances the model’s ability to learn fraud patterns by generating high-quality samples but also improves its resilience against highly concealed fraudulent activities. Experiments on real-world and public datasets demonstrate that GANHS outperforms its competitive peers, with gains of 0.5%–8.7% in average$F_{1}$-Score and 1.0%–7.0% in G-mean, highlighting its strong potential for improving the reliability and effectiveness of TFD systems in complex, high-risk financial scenarios.

Credit card fraud detection (CCFD) is an important issue concerned by financial institutions. Existing methods generally employ aggregated or raw features as their representations to train their detection models. Yet such features tend to fall short of effectively exposing the characteristics of various frauds. In this work, we propose a spatial-temporal gated network (STGN) to automatically learn new informative transactional representations containing users’ transactional behavioral information for CCFD. A gated recurrent neural net unit is specifically constructed with a time-aware gate and location-aware gate to extract users’ spatial and temporal transactional behaviors. A spatial-temporal attention module is designed to expose the transaction motive of users in their historical transactional behaviors, which allows the proposed model to better extract the fraudulent characteristics from successive transactions with time and location information. A representation interaction module is offered to make rational decisions and learn compositive transactional representations. A real-world transaction dataset is used in experiments to verify the efficacy of the learned new representations. The results demonstrate that our proposed model outperforms the state-of-the-art ones, thus greatly advancing the field of CCFD.Note to Practitioners—The features of transaction records reflect the characteristics of users’ transactional behaviors. Therefore, effective features are critical for accurate CCFD. However, fraudsters often pretend to be legitimate users during transactions to deceive the CCFD system. As a result, fraudulent behaviors become concealed within legitimate ones, signifying that original features are inadequate for accurate CCFD. Thus, it is imperative for researchers and practitioners to extract new features that can well expose fraud characteristics. While existing methods employing some transaction aggregation strategies can spot certain fraudulent behaviors, they fail to clearly cluster all the anomalous behaviors and distinguish them from legitimate behaviors. Therefore, this work is driven by the urgent demand to extract new informative features for CCFD. Its primary focus is to unveil the aggregation of fraudulent transactional behaviors from both temporal and spatial perspectives, enabling more accurate CCFD. Specifically, this work introduces a new STGN model that automatically learns new transactional representations incorporating users’ transactional behavioral information for CCFD. By comprehensively considering the time interval and location interval of consecutive user transactions, we thoroughly reveal the temporal and spatial aggregation of fraudulent behavior, which provides valuable insights for CCFD practitioners: 1) employing features that integrate the behavioral characteristics of fraudsters instead of the original features can enhance the model’s capability to identify frauds, and 2) taking into account the time and location intervals of users’ consecutive historical transactions can better uncover the behavioral characteristics of fraudsters.

Since minority samples are substantially less common than majority samples, many industrial applications, such as credit card fraud detection (CCFD) and defective part identification, call for imbalanced classification. The performance of a classifier tends to suffer from the noisy samples in majority or minority classes. This work proposes a new undersampling scheme, called a clustering-based noisy-sample-removed undersampling scheme (NUS) for imbalanced classification. The majority class samples are first clustered. The distance of the majority class sample from the cluster center that is furthest away is used as the radius to build a hypersphere, with each cluster’s center assumed to be a spherical center. We determine the Euclidean distance between the center of a cluster and each minority sample to find whether they are in the hypersphere or not. Afterward, we exclude noisy samples from the minority class. The noisy samples of majority classes are removed by using the same procedure. Second, we propose an NUS, which combines noisy sample removal with undersampling techniques. Finally, to prove the effectiveness of NUS, we integrate NUS with the basic classifiers random forest (RF), decision tree (DT), and logistics regression (LR). We conduct their comparison with seven undersampling, oversampling, and noisy-sample-removed methods. This work performs experiments on 13 public and three real transaction datasets related to e-commerce. The results show that NUS plays a positive role in promoting existing classifiers’ performance.

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn a new representation for each transaction record based on the historical transactions of users in order to capture fraudulent patterns accurately and, thus, automatically detect a fraudulent transaction. We propose a novel model by improving long short-term memory with a time-aware gate that can capture the behavioral changes caused by consecutive transactions of users. A current-historical attention module is designed to build up connections between current and historical transactional behaviors, which enables the model to capture behavioral periodicity. An interaction module is designed to learn comprehensive and rational behavioral representations. To validate the effectiveness of the learned behavioral representations, experiments are conducted on a large real-world transaction dataset provided to us by a financial company in China, as well as a public dataset. Experimental results and the visualization of the learned representations illustrate that our method delivers a clear distinction between legitimate behaviors and fraudulent ones, and achieves better fraud detection performance compared with the state-of-the-art methods.

With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure the safety of their user accounts. Existing models generally utilize original features or manually aggregated features as their transactional representations, while they fail to reveal the hidden fraudulent behaviors. In this work, we propose a novel model to extract transactional behaviors of users and learn new transactional behavioral representations for credit card fraud detection. Considering the characteristics of transactional behaviors, two time-aware gates are designed in a recurrent neural net unit to learn long- and short-term transactional habits of users, respectively, and to capture behavioral changes of users caused by different time intervals between their consecutive transactions. A time-aware-attention module is proposed and employed to extract the behavioral information from their consecutive historical transactions with time intervals, which enables the proposed model to capture behavioral motive and periodicity inside their historical transactional behaviors. An interaction module is designed to learn more comprehensive and rational representations. To prove the effectiveness of the learned transactional behavioral representations, experiments are conducted on a large real-world transaction dataset and a public one. The results show that the learned representation can well distinguish fraudulent behaviors from legitimate ones, and the proposed method can improve the performance of credit card fraud detection in terms of various evaluation criteria over the state-of-the-art methods.

Fraud detection has attracted significant attention in financial institutions, especially utilizing some artificial intelligent methods to automatically detect fraudulent transactions. With the study and application of intelligent fraud detection technology, scholars found that the representation learning method can reveal more information about fraud patterns, which is also crucial for detection task. Therefore, in this work, we present a novel method for detecting fraud transactions by combining two modules learning hidden information at different levels of data in a unified framework. To address and explore the deep representation of features of transaction behaviors, we propose a two-level attention model to capture them by integrating two data embeddings at the data sample level and the feature level. In particular, the sample-level attention model captures the detailed information more centrally that is difficult to determine; the feature-level attention model extends the information of feature dependences. We further combine them to train a final fraud detection model. Extensive experiments are conducted using a data set provided by a financial company in China and several public financial data sets. The results confirm the effectiveness of our proposed method in detecting fraudulent transactions compared with other state-of-the-art methods.