A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.

A Comparative Study of Network Securities on Mobile Transaction

In recent years, the number of mobile transactions has skyrocketed. Because mobile payments are made on the fly, many consumers prefer the method to the traditional local payment approach. The rise in mobile payments has inspired this study into the security of mobile networks in order to instill trust in those who may be involved in the transaction in some way. This report is a precursor to explain and compare some of the most popular wireless networks that enable mobile payments, from a security standpoint, this research presents, explains, and compares some of the most common wireless networks that enable mobile payments. Threat models in 3G with connections to GSM, WLAN, and 4G networks are classified into four categories: attacks on privacy, attacks on integrity, attacks on availability, and assaults on authentication. In addition, we offer classification countermeasures which are divided into three categories: cryptographic methods, human factors, and intrusion detection methods. One of the most important aspects we analyze is the security procedures that each network employs. Since the security of these networks is paramount, it gives hope to subscribers. In summary, the study aims to verify if mobile payments offer acceptable security to the average user.

Augmenting integrity and scalability in mobile payment applications using blockchain

The transactional information from the mobile wallets is offloaded from the mobile device to the mobile transaction server. The transaction involves various communication standards, confidential transaction information to ensure flawless transaction of data. There exist several encryption techniques to preserve confidentiality, hashing schemes to prove the integrity, signature schemes to prove the identity in the mobile transaction using mobile wallet applications. Even though mobile wallet possesses secure algorithms, the transactions are facing security issues such as double spending, lack of dispute redressal issue, lack of forward secrecy, lack of anonymity in the transaction and security. Therefore, Blockchain based Mobile transaction Scheme is proposed to solve the security issues including integrity, double spending and improve scalability. This paper presents a strategy which implements blockchain framework by using irreversible keys for mobile wallet applications. The proposed scheme proves to be secure against the security attacks and enhances integrity and scalability compared to the existing schemes.

A Context-Driven Commit Protocol for Enhancing Transactional Services Performance in Pervasive Environments

The proposed techniques for wireless environments during the last decade have limited support for dynamically changing environments. Due to its nature, the mobile computing environment is extremely dynamic and subject to rapid and unpredictable changes. Similarly, the characteristics of mobile applications affect their transactional requirements. The challenge is to reflect on solutions offering more flexibility and adaptability. In this article, the contribution was focused mainly on the problem of atomic commit that ensures the atomicity property. The trail of adapting mobile transaction commit protocols to context changes has been explored. This has led to the formalization of a flexible transaction model CATSM that supports adaptable properties and a commit protocol CA-TCP that enables adaptation to application requirements and mobile context in terms of transactional properties and execution cost. An architecture based on the concept of adaptation policy has also been designed for the implementation of the proposed solution.

An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation

Financial fraud under IoT environment refers to the unauthorized use of mobile transaction using mobile platform through identity theft or credit card stealing to obtain money fraudulently. Financial fraud under IoT environment is the fast-growing issue through the emergence of smartphone and online transition services. In the real world, a highly accurate process of financial fraud detection under IoT environment is needed since financial fraud causes financial loss. Therefore, we have surveyed financial fraud methods using machine learning and deep learning methodology, mainly from 2016 to 2018, and proposed a process for accurate fraud detection based on the advantages and limitations of each research. Moreover, our approach proposed the overall process of detecting financial fraud based on machine learning and compared with artificial neural networks approach to detect fraud and process large amounts of financial data. To detect financial fraud and process large amounts of financial data, our proposed process includes feature selection, sampling, and applying supervised and unsupervised algorithms. The final model was validated by the actual financial transaction data occurring in Korea, 2015.