In recent years, the increasing prevalence of mobile social network fraud has led to significant distress and depletion of personal and social wealth, resulting in considerable economic harm. Graph neural networks (GNNs) have emerged as a popular approach to tackle this issue. However, the challenge of graph imbalance, which can greatly impede the effectiveness of GNN-based fraud detection methods, has received little attention in prior research. Thus, we are going to present a novel cost-sensitive graph neural network (CSGNN) in this article. Initially, reinforcement learning is utilized to train a suitable sampling threshold, followed by neighbor sampling based on node similarity, which helps to alleviate the graph imbalance issue preliminarily. Subsequently, message aggregation is executed on the sampled graph using GNN to obtain node embeddings. Concurrently, the optimization objective for the cost matrix is formulated using the sample histogram matrix, scatter matrix, and confusion matrix. The cost matrix and GNN are collaboratively optimized through the backpropagation algorithm. Ultimately, the derived cost-sensitive node embedding is employed for fraudulent node detection. Furthermore, this study provides a theoretical demonstration of the effectiveness of adaptive cost-sensitive learning in GNN. Extensive experiments are carried out on two publicly accessible real-world mobile network fraud datasets, revealing that the proposed CSGNN effectively addresses the graph imbalance issue while outperforming state-of-the-art algorithms in detection performance. The CSGNN code and datasets can be accessed at https://github.com/xxhu94/CSGNN.

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a significant increase in telecom fraud, which severely dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, caused by the Pareto principle, brings severe challenges to graph data mining. This emerging and complex issue has received limited attention in prior research. In this paper, we propose a G raph AT tention network with CO st-sensitive BO osting (GAT-COBO) for the graph imbalance problem. First, we design a GAT-based base classifier to learn the embeddings of all nodes in the graph. Then, we feed the embeddings into a well-designed cost-sensitive learner for imbalanced learning. Next, we update the weights according to the misclassification cost to make the model focus more on the minority class. Finally, we sum the node embeddings obtained by multiple cost-sensitive learners to obtain a comprehensive node representation, which is used for the downstream anomaly detection task. Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is also helpful for solving the widespread over-smoothing problem in GNNs. The GAT-COBO code and datasets are available at https://github.com/xxhu94/GAT-COBO.

This paper proposes a systematic analysis method for 5G Non-Access Stratum Signalling security based on formal analysis, which has identified 10 new 5G protocol vulnerabilities, and an improved PKI security mechanism targeted at eliminating these vulnerabilities. Firstly, the 5G system, state transition properties and security properties were abstracted from 3GPP specifications. To mimic an attacker’s behavior, a Dolev-Yao adversary model was constructed in the 5G model by empowering it with NAS signalling security testing knowledge and reasonable security capabilities in the wireless channel. Then we used the TAMARIN prover to verify all the abstracted properties one by one and discovered some protocol vulnerabilities based on the counterexamples found. We further verified these vulnerabilities on the testbed and identified 10 new 5G protocol vulnerabilities. Moreover, we analyzed the root cause of these vulnerabilities and concluded that all of them were caused by the unconditional trust between UE and gNodeB. Therefore, we propose an improved PKI mechanism based on the existing asymmetric encryption of 5G. Besides the existing public-private key pair of the home network, we introduce a new pair of asymmetric keys in the gNodeB to encrypt and sign the signalling message sent to UE. The gNodeB can be connected only when the verification succeeds and then the RRC connection can be initiated. This mechanism can effectively avoid all the vulnerabilities found in this paper.

Link prediction in directed networks has always been a hot topic in many fields including network science, information system and data mining. Intuitively, once links are endowed with certain orientations, their reciprocate nature can potentially provide extra information for guiding link prediction. However, the role of reciprocal links in the formation of directed closure triads and their ability to enhance link prediction accuracy are not thoroughly investigated yet in existing works. In this paper, we first design an empirical test to investigate the role of reciprocal links in different types of directed networks by taking advantage of null models. Subsequently, based on solid evidence of the empirical test, two novel weighting mechanisms for link prediction are proposed utilizing reciprocity as extra information. The performance of proposed methods is comprehensively studied on eight realistic networks compared with several groups of benchmarks. Experimental results indicate that the proposed methods are more effective and robust than two state-of-the-art weighting methods and eight well-performing similarity indices.

With the rapid evolution of mobile communication networks, the number of subscribers and their communication practices is increasing dramatically worldwide. However, fraudsters are also sniffing out the benefits. Detecting fraudsters from the massive volume of call detail records (CDR) in mobile communication networks has become an important yet challenging topic. Fortunately, Graph neural network (GNN) brings new possibilities for telecom fraud detection. However, the presence of the graph imbalance and GNN oversmoothing problems makes fraudster detection unsatisfactory. To address these problems, we propose a new fraud detector. First, we transform the user features with the help of a multilayer perceptron. Then, a reinforcement learning-based neighbor sampling strategy is designed to balance the number of neighbors of different classes of users. Next, we perform user feature aggregation using GNN. Finally, we innovatively treat the above augmented GNN as weak classifier and integrate multiple weak classifiers using the AdaBoost algorithm. A balanced focal loss function is also used to monitor the model training error. Extensive experiments are conducted on two open real-world telecom fraud datasets, and the results show that the proposed method is significantly effective for the graph imbalance problem and the oversmoothing problem in telecom fraud detection.