Fraud detection aims to identify fraudsters from normal users. In graph environments, both fraudsters and normal users are modeled as nodes, while edges represent the connections between them. However, fraudulent nodes in the real world often camouflage themselves by establishing numerous fake connections with normal nodes, making them challenging to be identified. Existing fraud detection methods struggle to address this issue, they utilize graph neural networks to aggregate normal informations from normal neighbors, which leads to the smoothing of the fraudulent information. Furthermore, these methods exhibit poor generalization performance as they are unable to detect new fraudsters which not present in the training process. To overcome these limitations, this paper proposes GFAN, a novel model based on Graph Feature enhAncement Network. Specifically, GFAN introduces a specific semantic extraction module to screen and delete fake connections by evaluating the confidence level of edge presence. Additionally, GFAN provides a representation enhanced co-training module that highlights camouflaged fraudulent representations by training the small sphere and large margin support vector data description. Experimental results show that GFAN outperforms other competitive graph-based fraud detectors on public datasets. The GFAN code is available at: https://github.com/scu-kdde/OAM-GFAN-2023.

Abstract Temporal heterogeneous graphs can model lots of complex systems in the real world, such as social networks and e-commerce applications, which are naturally time-varying and heterogeneous. As most existing graph representation learning methods cannot efficiently handle both of these characteristics, we propose a Transformer-like representation learning model, named THAN, to learn low-dimensional node embeddings preserving the topological structure features, heterogeneous semantics, and dynamic patterns of temporal heterogeneous graphs, simultaneously. Specifically, THAN first samples heterogeneous neighbors with temporal constraints and projects node features into the same vector space, then encodes time information and aggregates the neighborhood influence in different weights via type-aware self-attention. To capture long-term dependencies and evolutionary patterns, we design an optional memory module for storing and evolving dynamic node representations. Experiments on three real-world datasets demonstrate that THAN outperforms the state-of-the-arts in terms of effectiveness with respect to the temporal link prediction task.

An accurate and comprehensive risk assessment in a distribution cyber-physical system (DCPS) is essential to ensure its smooth operation, effective control, and exposure of hidden dangers and security implications. Unfortunately, prior risk assessment methods are mostly limited in two aspects. First, the current studies do not respect the complex network topology and intertwined devicewise dependencies, thereby failing to delineate and model the risk propagation mechanism in DCPS accurately. Second, the prior work tends to focus on gauging the risks underlying physical systems independently, overlooking the quantification of risk impact on physical systems incurred by the cyberattack. To fill the gap, in this article, we propose a unified risk assessment model that gauges the comprehensive security implication of DCPS in a Bayesian regime, in which the three key components, namely, the prior probability, posterior probability, and minimum load-loss ratio, are calculated via the cumulative densities, epidemic model, and optimal load shedding, respectively. We benchmark our proposed model on a public testbed, IEEE 39-bus system, with extensive experiments. The results substantiate the viability and effectiveness of our model and suggest that the vulnerability of DCPS is positively correlated with the three components in our Bayesian modeling. We hope this finding can shed some light on future research by providing a plausible apparatus for measuring the security implications of physical systems in DCPS under cyberattacks.

Reliable data storage is crucial to the production, transmission, transaction, consumption, and analysis of an Energy Internet (EI). Whereas mainstream distributed data storage seems to be a plausible solution, the existing methods suffer from a tradeoff between the storage overhead (incurred by the replicas of data encodings for lossless recovery) and the communication latency (due to the spiking network traffic resulting from massive queries of data replicas across devices). To balance this tradeoff, we propose aLightweight Dynamic Storage Algorithm based on Adaptive Encoding(LDSA-AE) approach for EI data storage. Our key idea is to classify the data into active and inactive categories, where the active data are most likely to be accessed and thus corrupted in high frequencies. As such, wherever the active data are housed, the replicas of them can be proactively allocated into a set of nearby devices. The main challenges are to realize the classification in real-time and to tailor encoding methods for the active and inactive separately in correspondence to their own characteristics. To overcome these, our LDSA-AE 1) proposes a novel density-based clustering algorithm to tackle performance data classification in an online and unsupervised fashion and 2) leverages Minimum Density RAID-6 (MDR) code and Cauchy Reed-Solomon (CRS) code for active and inactive data encodings, respectively, striving to ensure data storage with low overhead, low latency, high reliability, and high throughput at once. A theoretical analysis substantiates the viability and effectiveness of our proposed LDSA-AE approach. We also prototype our LDSA-AE on a real-world server testbed, and the empirical study suggests the superiority of our approach over the state-of-the-art distributed storage schemes for EI in terms of storage overhead, repair throughput, and reliability.

Analyzing user behavior characteristics in a complex power grid environment is essential for user behavior planning and resource coordination optimization. Traditional user behavior analysis methods based on model-driven and causal analysis have the disadvantages of strong subjectivity and physical models that are difficult to deal with the randomness and uncertainty of user behavior in complex grid environments. In this paper, we use unsupervised learning methods to analyze user behavior in complex power grid environments, and propose user behavior analysis methods based on stacked autoencoder and clustering. We first reduce the complexity of user behavior data by proposing adaptive feature selection algorithm of user behavior based on stacked autoencoder and unsupervised learning (AFS-SAEUL). Finally, we build a user behavior analysis model based on adaptive feature selection and improved clustering (UBA-AFSIC). The model improved the performance of unsupervised classification of user behavior by fusing the adaptive generation strategy of the initial cluster centers. The simulation experiment results on two real electricity datasets and one public electric vehicle charging dataset show that compared with the existing feature selection algorithm and clustering algorithm, the algorithms proposed in this paper have higher feature selection rate and better clustering performance.

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

Image thresholding is one of the most imperative practices to accomplish image segmentation, image compression and target recognition. This has been widely studied over the past few decades. However, the multilevel thresholding computationally takes more time when the threshold number increases. Hence, this paper proposes a honey bee mating-based algorithm (HBMA) based on Bayesian theorem and the characters of intensity images for image segmentation to save computation time. This kind of HBMA is called as Bayesian Honey Bee Mating Algorithm (BHBMA). Moreover, we adopt a population initialization strategy to make the search more efficient, according to the characters of multilevel thresholding in an image arranged from a low gray level to a high one. Extensive experiments have shown that BHBMA can deliver more effective and efficient results to be applied in complex image processing such as automatic target recognition, compared with state-of-the-art population-based thresholding methods.