Online
Modeling of Proactive Moderation System for
Auction
Fraud Detection
ABSTRACT:
We consider the problem of building
online machine-learned models for detecting auction frauds in e-commerce web
sites. Since the emergence of the world wide web, online shopping and online
auction have gained more and more popularity. While people are enjoying the
benefits from online trading, criminals are also taking advantages to conduct fraudulent
activities against honest parties to obtain illegal profit. Hence proactive
fraud-detection moderation systems are commonly applied in practice to detect
and prevent such illegal and fraud activities. Machine-learned models,
especially those that are learned online, are able to catch frauds more
efficiently and quickly than human-tuned rule-based systems. In this paper, we
propose an online probit model framework which takes online feature selection,
coefficient bounds from human knowledge and multiple instance learning into
account simultaneously. By empirical experiments on a real-world online auction
fraud detection data we show that this model can potentially detect more frauds
and significantly reduce customer complaints compared to several baseline
models and the human-tuned rule-based system.
EXISTING SYSTEM
The traditional online shopping business model allows sellers to sell a
product or service at a preset price, where buyers can choose to purchase if
they find it to be a good deal. Online auction however is a different business
model by which items are sold through price bidding. There is often a starting
price and expiration time specified by
the sellers. Once the auction starts, potential buyers bid against each other,
and the winner gets the item with their highest winning bid.
PROPOSED SYSTEM:
We propose an
online probit model framework which takes online feature selection, coefficient
bounds from human knowledge and multiple instance learning into account
simultaneously. By empirical experiments on a real-world online auction fraud
detection data we show that this model can potentially detect more frauds and
significantly reduce customer complaints compared to several baseline models
and the human-tuned rule-based system. Human experts with years of experience
created many rules to detect whether a user is fraud or not. If the fraud score
is above a certain threshold, the case will enter a queue for further
investigation by human experts. Once it is reviewed, the final result will be
labeled as boolean, i.e. fraud or clean. Cases with higher scores have higher
priorities in the queue to be reviewed. The cases whose fraud score are below
the threshold are determined as clean by
the system without any human judgment.
MODULE:
Rule-based
features
Selective
labeling
Fraud churn
User Complaint
MODULE DESCRIPTION:
Rule-based features:
Human experts with years of experience created many rules to detect
whether a user is fraud or not. An example of such rules is “blacklist”, i.e.
whether the user has been detected or complained as fraud before. Each rule can
be regarded as a binary feature that indicates the fraud likeliness.
Selective labeling:
If the fraud score is above a certain threshold, the case will enter a
queue for further investigation by human experts. Once it is reviewed, the
final result will be labeled as boolean, i.e. fraud or clean. Cases with higher
scores have higher priorities in the queue to be reviewed. The cases whose
fraud score are below the threshold are determined as clean by the system
without any human judgment.
Fraud churns:
Once one case is labeled as fraud by human experts, it is very likely
that the seller is not trustable and may be also selling other frauds; hence all
the items submitted by the same seller are labeled as fraud too. The fraudulent seller along with his/her cases
will be removed from the website immediately once detected.
User Complaint:
Buyers can file complaints to claim loss if they are recently deceived
by fraudulent sellers. The Administrator views the various type of complaints
and the percentage of various type complaints. The complaints values of a
products increase some threshold value the administrator set the trustability
of the product as Untrusted or banded. If the products set as banded, the user
cannot view the products in the website.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
•
System : Pentium IV 2.4 GHz.
•
Hard
Disk : 40 GB.
•
Floppy
Drive : 1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse : Logitech.
•
Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
•
Operating system : - Windows XP.
•
Coding Language : J2EE
•
Data Base : MYSQL
REFERENCE:
Liang Zhang, Jie Yang, Belle Tseng, “Online
Modeling of Proactive Moderation System for Auction Fraud Detection”, ACM
International Conference, April 16–20, 2012.
