Credit card fraud events take place frequently and then result in huge financial losses. Criminals can use some technologies such as Trojan or Phishing to steal the information of other people's credit cards. Therefore, an effictive detection method is important since it identify a time when criminal uses stolen consume. One make full historical transaction data including normal transactions ones obtain normal/fraud behavior features based on machine learning techniques, utilize these check...

Fog computing, also called "clouds at the edge," is an emerging paradigm allocating services near devices to improve quality of service (QoS). The explosive prevalence Internet Things, big data, and fog computing in context cloud makes it extremely challenging explore both resource scheduling strategy so as efficiency resources utilization, satisfy users' QoS requirements, maximize profit providers users. This paper proposes a allocation for based on priced timed Petri nets (PTPNs), by which...

Particle swarm optimization (PSO) algorithm is a population-based stochastic technique. It characterized by the collaborative search in which each particle attracted toward global best position (gbest) and its own (pbest). However, all of particles' historical promising pbests PSO are lost except their current pbests. In order to solve this problem, paper proposes novel composite algorithm, called memory-based (HMPSO), uses an estimation distribution estimate preserve information Each has...

With the rapid development of electronic commerce, number transactions by credit cards are increasing rapidly. As online shopping becomes most popular transaction mode, cases fraud also increasing. In this paper, we propose a novel detection method that composes four stages. To enrich cardholder's behavioral patterns, first utilize cardholders' historical data to divide all cardholders into different groups such behaviors members in same group similar. We thus window-sliding strategy...

MapReduce has become a major computing model for data intensive applications. Hadoop, an open source implementation of MapReduce, been adopted by increasingly growing user community. Cloud service providers such as Amazon EC2 offer the opportunities Hadoop users to lease certain amount resources and pay their use. However, key challenge is that cloud do not have resource provisioning mechanism satisfy jobs with deadline requirements. Currently, it solely user's responsibility estimate...

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 most important problems is make load balancing among these stations. Different from common mobile networks, data traffic in networks can be observed having regularities spatial-temporal dimension due periodicity urban flow. By taking advantage this feature, we propose an online reinforcement...

Credit card fraud detection is an important study in the current era of mobile payment. Improving performance a model and keeping its stability are very challenging because users' payment behaviors criminals' often changing. In this article, we focus on obtaining deep feature representations legal transactions from aspect loss function neural network. Our purpose to obtain better separability discrimination features so that it can improve our keep stability. We propose new kind function,...

With the popularization of online shopping, transaction fraud is growing seriously. Therefore, study on detection interesting and significant. An important way detecting to extract behavior profiles (BPs) users based their historical records, then verify if an incoming a or not in view BPs. Markov chain models are popular represent BPs users, which effective for those whose behaviors stable relatively. However, with development it more convenient consume via Internet, diversifies users....

Datacenter-scale clusters are evolving toward heterogeneous hardware architectures due to continuous server replacement. Meanwhile, datacenters commonly shared by many users for quite different uses. It often exhibits significant performance heterogeneity multi-tenant interferences. The deployment of MapReduce on such presents challenges in achieving good application compared in-house dedicated clusters. As most implementations originally designed homogeneous environments, can cause...

Microservices are widely used for flexible software development. Recently, containers have become the preferred deployment technology microservices because of fast start-up and low overhead. However, container layer complicates task scheduling auto-scaling in clouds. Existing algorithms do not adapt to two-layer structure composed virtual machines containers, they often ignore streaming workloads. To this end, article proposes an Elastic Scheduling (ESMS) that integrates with auto-scaling....

AdaBoost is a boosting-based machine learning method under the assumption that data in training and testing sets have same distribution input feature space. It increases weights of those instances are wrongly classified process. However, does not hold many real-world sets. Therefore, extended to transfer (TrAdaBoost) can effectively knowledge from one domain another. TrAdaBoost decreases belong source but more suitable for case different distribution. Can it be improved some special...

With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure safety their user accounts. Existing models generally utilize original features or manually aggregated as transactional representations, while they fail reveal hidden fraudulent behaviors. In this work, we propose a novel model extract behaviors users learn new behavioral representations for card detection. Considering characteristics behaviors, two time-aware gates...

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn new representation for each transaction record based on the historical transactions of users order capture patterns accurately and, thus, automatically detect transaction. We propose novel model by improving long short-term memory with time-aware gate that can behavioral changes caused consecutive users. A current-historical attention module designed...

Distributed denial of service (DDoS) attacks have become one the main factors restricting development internet vehicles (IoV). Although some intelligent reinforcement learning based methods been introduced to mitigate DDoS attacks, there are still many constraints in training process, such as long time and dependence on large labeled data. In this paper, we propose a transfer double deep Q-network (DDQN) detection method for IoV. By constructing Kalman filter model, can constantly improve...

Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. How to upcycle DNNs adapt them target task remains an important open problem. Unsupervised Domain Adaptation (UDA), especially recently proposed Source-free (SFDA), has become a promising technology address this issue. Nevertheless, existing SFDA methods require that source share same label space, consequently being only applicable vanilla closed-set setting. In paper, we take one step...

Anti-money laundering (AML) is a classical data mining problem in finance applications. As well known, money (ML) critical to the effective operation of transnational and organized crime, which affects country's economy, government, social wellbeings. Financial services organizations facilitate movement have been enlisted by governments assist with detection prevention laundering, key tool fight reduce crime create sustainable economic development. In application AML, user identity financial...

Through vehicle-to-vehicle (V2V) communication, autonomizing a vehicle platoon can significantly reduce the distance between vehicles, thereby reducing air resistance and improving road traffic efficiency. The gradual maturation of control technology is enabling platoons to achieve basic driving functions, permitting large-scale scheduling planning, which essential for industrialized applications generates significant economic benefits. Scheduling planning are required in many aspects...

As there are various risks of failure in its execution, a composite web service (CWS) requires transactional mechanism to guarantee reliable execution. Though the existing selection methods have considered that properties may affect quality (QoS) such as execution time, some these can just give locally optimal CWS while others globally only under given fixed workflow. This paper addresses issue selecting and composing services via genetic algorithm (GA) gives transaction QoS-aware approach....

The rapid development of location-based social networks (LBSNs) provides people with an opportunity better understanding their mobility behavior which enables them to decide next location. For example, it can help travelers choose where go next, or recommend salesmen the most potential places deliver advertisements sell products. In this paper, a method for recommending points interest (POIs) is proposed based on collaborative tensor factorization (CTF) technique. Firstly, generalized...

In human society, which is organized by social hierarchies, resources are usually allocated unequally and based on status. this study, we analyze how being endowed with different statuses in a math competition affects the perception of fairness during asset allocation subsequent Ultimatum Game (UG). Behavioral data showed that when participants were high status, they more likely to reject unfair UG offers than low This effect status correlated activity right anterior insula (rAI) functional...