Money laundering is the process of hiding the origin of illicit funds to make them appear legitimate, thereby threatening the integrity of financial systems. To detect money laundering activities, graph neural networks (GNNs) have been widely adopted to model complex relational structures in transaction networks. However, a closer inspection of real-world money laundering cases reveals that launderers deliberately establish connections with multiple licit accounts to mask their illicit attributes. Such disguising behavior introduces network heterogeneity, which contradicts the fundamental assumption of homophily for most GNNs. Additionally, money launderers further conceal their activities by obscuring illicit fund flows through multihop transaction paths. This strategy poses a significant challenge for GNNs, as their limited receptive fields struggle to capture such long-range dependencies. To address these challenges, we propose a dual-view graph representation learning method, named DC-LCG, to detect money laundering. DC-LCG employs complementary local and contextual views to expose disguises and trace illicit flows, respectively. The local view implements a soft-label-guided dynamic grouping and aggregation method that separates nodes into illicit and licit groups, performing probability-weighted aggregations to mitigate network heterogeneity and expose disguises within transaction networks. The contextual view employs a dynamic path pruning method to filter licit nodes and enhance paths relevance, followed by multipath semantic fusion through transformer-based encoding to capture long-range dependencies across multihop transaction paths. A mutual attention mechanism integrates both views to create comprehensive node representations. Experiments on three public transaction datasets show that DC-LCG outperforms state-of-the-art baselines by 2%–10% across evaluation metrics.

Nowadays, cooperation between different organizations to form a cross-industry anti-fraud alliance by using federated learning (FL) is promising for transaction fraud detection. However, there are two challenges when building the federated learning-based fraud detection method. First, existing FL-based fraud detection methods cannot fully use participants' local data to better distinguish unknown types of abnormal fraud transactions. A lot of local data would be discarded due to the private set intersection process under the federated learning framework. Second, they mainly focus on the fraud detection performance but ignore the privacy and communication costs during the training of the fraud detection model. In response to the above challenges, we propose a privacy-aware (P) transaction fraud detection model with low (L) communication costs (C) under a vertical and horizontal joint federated learning framework in this paper, named PLC. Specifically, we merge horizontal federated learning and vertical federated learning to build a normal behavior model to improve the fraud detection performance for solving the first challenge. Meanwhile, we design a sparse factor-based SMC aggregation method to reduce communication costs without sacrificing detection performance for solving the second challenge. The experimental results show that the communication costs can be reduced by 46.7% and 55.4% under the ECC dataset and the Vesta dataset, respectively. The ablation experiment results show that our proposed method can provide good privacy protection performance with a small sparse factor. Our proposed fraud detection model can significantly improve the fraud detection performance under the joint federated learning framework.

Anti-money laundering (AML) is crucial to maintaining national financial security. Contemporary AML methods focus on homogeneous mining or unitary money laundering pattern. These methods ignore a characteristic of gang operation in money laundering. Thus, in this paper, we propose a multi-view graph-based hierarchical representation learning method, named MG-HRL, to mine organized money laundering groups. In particular, we extract multi-level representations of transaction subgraphs, including transaction features, user features, structural features, and high-order association features from multiple observational perspectives. To learn the correlation between users, we model transaction networks as heterogeneous information networks (HINs) and design six meta-paths related to money laundering scenarios to mine correlations among users. Combining with correlation representations of users, we propose a heterogeneous hypergraph representation learning method to learn high-order representations of transaction subgraphs. Through hierarchical representation learning, the MG-HRL achieves full exploration of money laundering groups. Finally, we conduct experiments on two public transaction datasets. The result shows that MG-HRL method performs better than other state-of-the-art baselines.

Currently, due to the different distribution of data for each user, many personalized federated learning (PFL) methods have emerged to meet the personalized needs of different users. However, existing methods have two problems: 1) in the aggregation process, the contribution between the internal layers of the client model is not considered and 2) it is difficult to match the quantitative weight information of both user privacy protection and performance with their qualitative preferences during the training process. Therefore, we first propose a framework for federated aggregation with interlayer personalized contribution named FedIPC, which completes model aggregation based on the contribution of internal layers and improves client model performance. Based on the above framework, we design a multiobjective federated optimization method based on adaptive preference indicators named FedAPI-nondominated sorting genetic algorithm II (NSGA-II). This method can match quantitative weight with qualitative user preferences and adaptively select for Pareto optimal solutions during the optimization process. Extensive experiments on two image datasets and a tabular dataset show that our proposed method not only accelerates model convergence, but also achieves good improvements in model performance. In addition, our proposed method can accurately match the qualitative preferences of users, balancing the performance of the model and privacy protection based on preferences.

Internet of Vehicles (IoV) improves traffic safety and efficiency by wireless communications among vehicles and infrastructures. To ensure secure communications in IoV, the problem of vehicle identity security must be solved before deployment. In this article, we propose a quick-response behavior-based vehicle identity verification method, called VLOG, for solving identity theft in IoV. This method is based on the idea of a vehicle usually having relatively stable traveling habit/behaivor. If we detect unusual behavior, the vehicle's identity may be stolen. VLOG captures vehicles’ latent behavior models from local and global two aspects, and further merges local and global models into a comprehensive behavior-based identity verification model. In the local part, we give a 2-D Gaussian model to fit the behavior data. In the global part, we learn vehicles’ traveling preferences under secure multiparty computation framework with considering the behavior volatility. The results of experiments based on a real-world vehicular trace dataset show the best performance of VLOG in terms of accuracy, F1 score, and cost. Meanwhile, VLOG also performs well in the area under the curve and precision-recall curve. Besides, since our model is preprepared, when a vehicle is required to be detected, the verification response time is short.

Federated learning is usually utilized as a fraud detection framework in the domain of financial risk management, which promotes the model accuracy without training data exchange. One of the challenges in federated learning is the GAN-based poisoning attack. The GAN-based poisoning attack is a type of intractable poisoning attack that causes global model accuracy degradation and privacy leak. Most of the existing defenses for GAN-based poisoning attack have the three problems: 1) dependence on validation datasets; 2) incompetence of dealing with incremental poisoning attack; and 3) privacy leak. To address the above problems, we present a privacy-aware and incremental defense (PID) method to detect malicious participants and protect privacy. In PID, we design a method to accumulate the offset of model parameters from participants in all current epochs to represent the moving tendency for model parameters. Thus, we can distinguish the adversaries from normal participants based on the accumulations in this incremental poisoning attack. We also use multiple trust domains to reduce the rate of misjudging benign participants as adversaries. Moreover, a differentiated differential privacy is utilized before the global model sending to protect the privacy of participants’ training datasets in PID. The experiments conducted on two real-world datasets under financial fraud detection scenario demonstrate that the PID reduces the fallout of adversaries detection (the rate of misjudging benign participants as adversaries) by at least 51.1% and improve the speed of detecting all malicious participants by at least 33.4% compared with two popular defense methods. Besides, the privacy preserving of PID is also effective.

Nowadays, a lot of studies unite multiple organizations to form an anti-fraud alliance to detect fraudulent transactions better using federated boosting tree algorithms. However, there are two challenges when building the federated boosting tree-based fraud detection model. First, the vertical federated learning (VFL) framework is not enough for the transaction fraud detection task because there are various participants in the same field (e.g., different banks) who cannot share data with others freely. And a lot of local data would be discarded due to the private set intersection process under the VFL framework. Second, there are still many privacy threats that can infer the data sequence information of the participants. Once the attackers illegally obtain the data sequence information, they can infer the raw data of the victims based on the data distribution. Specially, the instance spaces and score lists would also be maliciously exploited to launch a new data sequence inference attack that is currently indefensible. In response to the above challenges, we first propose a sequence inference attack that is the first work showing the vulnerabilities regarding the instance spaces and score lists. Then, we propose a federated boosting tree-based fraud detection method against (A) sequence (S) inference (I) attacks (A), named ASIA. ASIA method can combine the horizontal federated learning (HFL) framework with the VFL framework to better detect fraudulent transactions while defending against sequence inference attacks. Finally, we evaluate SIS attack and ASIA method in experiments based on two public fraud detection datasets: European Credit Card (ECC) and IEEE-CIS Fraud Detection (Vesta). The experimental results show that the sequences of the participants are at a high risk of being leaked when suffering SIS attack. Moreover, compared with several widely used federated boosting tree methods, ASIA method can significantly improve privacy-preserving performance without sacrificing fraud detection accuracy.

For the future open 5G Internet of Vehicles (IoV), due to the flexibility and load sharing, the popular network architecture of IoV proposed by many studies is the mobile-edge computing (MEC) framework combining with software-defined networking (SDN). However, under this architecture, moving vehicles and MEC devices are not like the cloud SDN with strong security protection. Thus, identity verification is an important security issue. We find that if the identity credentials of vehicles and infrastructures are obtained by adversaries (i.e., identity theft), the current cryptography-based authentication methods cannot cope with this problem. In this article, we propose a behavior-based verification method, named Crowd-Learning, by utilizing the idea of crowd in software-defined vehicular networks with a MEC framework. In Crowd-Learning, we design an incentive mechanism to stimulate some MEC infrastructures to provide accurate and appropriate amount of data for future correct behavior estimation. Without knowing the model of the dynamic environment, this incentive mechanism needs to apply reinforcement learning to let MEC infrastructures learn how to send data based on the current state. Our Crowd-Learning method verifies vehicles and reduces the verification latency by estimating the vehicle’s behavior in advance. Meanwhile, it verifies infrastructures during the process of reinforcement learning based on the idea of crowd intelligence. The fake infrastructures and anomalous vehicles expose themselves when learning. In experiments, we use the traffic simulation tool, called simulation of urban mobility (SUMO), to generate extensive vehicle traces and evaluate the performance of the Crowd-Learning verification method. The results show that the Crowd-Learning verification method can ensure high verification accuracy for vehicles and infrastructures with satisfying low verification latency.

With the development of 5G technology, mobile edge computing (MEC) is introduced into the construction of internet of vehicles (IoV). However, the distributed denial of services (DDoS) attacks become a serious problem in IoV under MEC. Although numbers of studies have been done on DDoS detection in common wired or wireless networks, they cannot satisfy the high dynamic requirement and cannot cope with the complex and diverse DDoS attacks in IoV. Fortunately, the data traffic flows in IoV exist potential and predictable space-time regularities. By employing reinforcement learning, we propose a feature adaption reinforcement learning approach based on the space-time flow regularities in IoV for DDoS mitigation, named FAST. In FAST, we elaborately design a combinational action space, and a reward function based on Kalman filter method and historical data traffic flows, which can make FAST to recognize DDoS attacks more quickly and accurately. Then through combining Q-learning and DDQN, FAST can select features and disconnect DDoS attacks adaptively according to the changes of the environment. In experiments, we evaluate the performance of FAST based on Shenzhen taxicab dataset. We simulate and inject DDoS attacks into Shenzhen taxicabs through two DDoS simulation tools named ‘ddosflowgen’ and ‘hping3’. The experimental results show that FAST has a high quality in detecting multiple types of DDoS attacks compared with other detection methods.

The Internet of Vehicles (IoV) is a subapplication of the Internet of Things in the automotive field. Large amounts of sensor data require to be transferred in real-time. Most of the routing protocols are specifically targeted to specific situations in IoV. But communication environment of IoV usually changes in the space-time dimension. Unfortunately, the traditional vehicular networks cannot select the optimal routing policy when facing the dynamic environment, due to the lack of abilities of sensing the environment and learning the best strategy. Sensing and learning constitute two key steps of the cognition procedure. Thus, in this paper, we present a software defined cognitive network for IoV (SDCIV), in which reinforcement learning and software defined network technology are considered for IoV to achieve cognitive capability. To the best of our knowledge, this paper is the first one that can give the optimal routing policy adaptively through sensing and learning from the environment of IoV. We perform experiments on a real vehicular dataset to validate the effectiveness and feasibility of the proposed algorithm. Results show that our algorithm achieves better performance than several typical protocols in IoV. We also show the feasibility and effectiveness of our proposed SDCIV.

Recently, a number of technologies have been developed to promote vehicular networks. When vehicles are associated with the heterogeneous base stations (e.g., macrocells, picocells, and femtocells), one of the most important problems is to make load balancing among these base stations. Different from common mobile networks, data traffic in vehicular networks can be observed having regularities in the spatial-temporal dimension due to the periodicity of urban traffic flow. By taking advantage of this feature, we propose an online reinforcement learning approach, called ORLA. It is a distributed user association algorithm for network load balancing in vehicular networks. Based on the historical association experiences, ORLA can obtain a good association solution through learning from the dynamic vehicular environment continually. In the long run, the real-time feedback and the regular traffic association patterns both help ORLA cope with the dynamics of network well. In experiments, we use QiangSheng taxi movement to evaluate the performance of ORLA. Our experiments verify that ORLA has higher quality load balancing compared with other popular association methods.

Urban traffic condition usually serves as a basic information for some intelligent urban applications, e.g., intelligent transportation system. But the acquisition of such information is often costly due to the dependency on equipments such as cameras and loop detectors. Crowdsensing can be utilized to gather vehicle-sensed data for traffic condition estimation. This way of data collection is economic. However, it has the problems of data uploading efficiency and data usage effectiveness. To deal with these problems, in this paper, we take into account the topology of the road net. We divide the road net into Road Sections and Junction Areas. Based on this division, we introduce a two-phased data collection and processing scheme named RTS (Road Topology based Scheme). It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles is first processed and integrated by a sponsor vehicle. This sponsor vehicle will calculate the traffic condition locally. Both the selection of the sponsor and the calculation of the traffic condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for road sections unreached by vehicular data in a global vision. We conduct extensive experiments based on real vehicle trace data. The results indicate that, our design can commendably handle the problems of efficiency and effectiveness in the vehicular-crowdsensing-data based traffic condition evaluation.

In this paper, we mainly investigate capacity scaling laws of the mobile ad hoc social networks (MAHSNs)where social networking applications are implemented over the underlying mobile ad hoc networks. We model the real-world mobility pattern of mobile social users by introducing a clustered model that defines two levels of mobility, i.e., Strong mobility and weak mobility, according to the impacts of mobility on the gain of network capacity. To address the formation of social relationships among mobile social users, we adopt a distance and density aware social model called population-distance-based model that comprehensively and practically takes account of the clustering levels of friendship degree and distribution. Under those models, we derive the capacity scaling laws for social-broadcast sessions in MAHSNs. The results provide the exploratory insights into the impacts of users' mobility patterns and the formation of social relationships on the network capacity of MAHSNs.

This paper aims to design an efficient data forwarding scheme based on local activity and social similarity(LASS) for mobile social networks (MSNs). Various definitions of social similarity have been proposed as the criterion for relay selection, which results in various forwarding schemes. The appropriateness and practicality of various definitions determine the performances of these forwarding schemes. A popular definition has recently been proven to be more efficient than other existing ones, i.e., the more common interests between two nodes, the larger social similarity between them. In this work, we show that schemes based on such definition ignore the fact that members within the same community, i.e., with the same interest, usually have different levels of local activity, which will result in a low efficiency of data delivery. To address this, in this paper, we design a new data forwarding scheme for MSNs based on community detection in dynamic weighted networks, called Local-Activity and Social-Similarity, taking into account the difference of members' internal activity within each community, i.e., local activity. To the best of our knowledge, the proposed scheme is the first one that utilizes different levels of local activity within communities. Through extensive simulations, we demonstrate that LASS achieves better performance than state-of-the-art protocols.

In this paper, we investigate capacity scaling laws of wireless social networks under the social-based session formation. We model a wireless social network as a three-layered structure, consisting of the physical layer, social layer, and session layer; and we introduce a cross-layer distance & density-aware model, called the population-based formation model, under which: 1) for each node vk, the number of its friends/followers, denoted by qk, follows a Zipf's distribution with degree clustering exponent g; 2) qkanchor points are independently chosen according to a probability distribution with density function proportional to (Ek,X)-β, where Ek;Xis the expected number of nodes (population) within the distance |vk-X| to vk, and β is the clustering exponent of friendship formation; 3) finally, qknodes respectively nearest to those qkanchor points are selected as the friends of vk. We present the general density function of social relationship distribution, with general distribution of physical layer, serving as the basis for studying general capacity of wireless social networks. As the first step of addressing this issue, for the homogeneous physical layer, we derive the social-broadcast capacity under both generalized physical and protocol interference models, taking into account general clustering exponents of both friendship degree and friendship formation in a 2-dimensional parameter space, i.e., (γ,β) ϵ[0,∞)2. Importantly, we notice that the adopted model with homogenous physical layer does not sufficiently reflect the advantages of the population-based formation model in terms of realistic validity and practicability. Accordingly, we introduce a random network model, called the center-clustering random model (CCRM) with node distribution exponent δ ϵ [0, ∞), highlighting the clustering and inhomogeneity property in real-life networks, and discuss how to further derive more general network capacity over 3-dimensional parameter space (δ,γ,β) ϵ [0, ∞)3based on our results over (γ,β) ϵ [0, ∞)2.

In this paper, we study two tightly coupled issues, space-crossing community detection and its influence on data forwarding in mobile social networks (MSNs). We propose a communication framework containing the hybrid underlying network with access point (AP) support for data forwarding and the base stations for managing most of control traffic. The concept of physical proximity community can be extended to be one across the geographical space, because APs can facilitate the communication among long-distance nodes. Space-crossing communities are obtained by merging some pairs of physical proximity communities. Based on the space-crossing community, we define two cases of node local activity and use them as the input of inner product similarity measurement. We design a novel data forwarding algorithm Social Attraction and Infrastructure Support (SAIS), which applies similarity attraction to route to neighbor more similar to destination, and infrastructure support phase to route the message to other APs within common connected components. We evaluate our SAIS algorithm on real-life datasets from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs. Based on this new type of community, SAIS achieves a better performance than existing popular social community-based data forwarding algorithms in practice, including Simbet, Bubble Rap and Nguyen's Routing algorithms.

In this paper, we study two tightly coupled issues: space-crossing community detection and its influence on data forwarding in Mobile Social Networks (MSNs) by taking the hybrid underlying networks with infrastructure support into consideration. The hybrid underlying network is composed of large numbers of mobile users and a small portion of Access Points (APs). Because APs can facilitate the communication among long-distance nodes, the concept of physical proximity community can be extended to be one across the geographical space. In this work, we first investigate a space-crossing community detection method for MSNs. Based on the detection results, we design a novel data forwarding algorithm SAAS (Social Attraction and AP Spreading), and show how to exploit the space-crossing communities to improve the data forwarding efficiency. We evaluate our SAAS algorithm on real-life data from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs in terms of delivery ratio and delay. Based on this new type of community, SAAS achieves a better performance than existing social community-based data forwarding algorithms in practice, including Bubble Rap and Nguyen's Routing algorithms.