Leveraging Product Characteristics For Online Collusive Detection in Big Data Transactions
Leveraging Product Characteristics For Online Collusive Detection in Big Data Transactions
Leveraging Product Characteristics For Online Collusive Detection in Big Data Transactions
Online fraud transaction has been a big concern for e-business platform. As the development of big data
technology, e-commerce users always evaluate the sellers according to the reputation scores supplied
by the platform. The reason why the sellers prefer chasing high reputation scores is that high reputations
always bring high profit to sellers. By collusion, fraudsters can acquire high reputation scores and it
will attract more potential buyers. It has been a crucial task for the e-commerce website to recognizing
the fake reputation information. E-commerce platforms try to solve this continued and growing problem
by adopting data mining techniques. With the high development of the Internet of things (IOT), big data
plays a crucial role in economic society. Big data brings economic growth in different domains. It
supplies support to the management and decision-making ability in e-business through analyzing
operational data. In online commerce, big data technology also helps in providing users with a fair and
healthy reputation system, which improves shopping experience. This paper aims to put forward a
conceptual framework to extract the characteristics of fraud transaction including individual and
transaction related indicators. It also contains two product features: product type and product nature.
The two features obviously enhance the accuracy of fraud detection. A real-world dataset is used to
verify the effectiveness of the indicators in the detection model which is put forward to recognize the
fraud transactions from the legitimate ones.
EXISTING SYSTEM
After a transaction, online business platform provides evaluation system to accumulate the participants’
reputation. The participants rate their counterparts after each transaction (Chang et al.,2016). As online
reputation score has become increasingly influential in helping online shoppers to make purchase
decisions, cyber criminals have strong financial motivation to undertake collusive transactions (Zhao et
al., 2016). Collusive fraud has emerged as a major threat to e-commerce platform. The oversimplified
design of online reputation system creates a haven for collusive fraudsters. Con artists exploit the
convenience of registration to cheat benign buyers (Sidney et al.,2014). So, developing an effective
method to maintain healthy reputation system is a crucial task
PROCESSED SYSTEM
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