Recognizing The Theft of Identity Using Data Mining
نویسندگان
چکیده
Identity fraud is the great matter of concern in the field of the e-commerce. Identity Fraud is more than a security issue it is a financial burden as in transaction and application domain the culprit can make serious problems for the victims as they can be affected with unethical activity by using victims’ private information for the economic gain. Application fraud is one of the prominent example of identity fraud where the thief can use victims’ personal information for issuing the credit card account or loan. To counter this problem data mining based two step recognition system is proposed. This system contains two algorithms Communal Detection (CD) which checks for multi-attribute link and Spike Detection (SD) which checks for single attribute link. CD algorithm targets the communal relationships of the dataset while SD algorithm finds the spikes between the duplicates in the dataset. Together these two algorithms can be used for the theft detection in the application fraud. Also detect several attacks. Keywords— Anomaly Detection, Application Domain, Data Stream, Identity Theft.
منابع مشابه
The Consequences of the Identity Theft Fear in the Sport Products Online Shopping From the Perspective of Physical Education Students
Background. Today, online shopping has become one of the most important components of modern marketing that had both positive and negative consequences for customers. Objectives. The purpose of present research was to study the consequences of fear of identity theft in sport products online shopping from the perspective of physical education students. Methods. The present study is a descripti...
متن کاملDetecting Suspicious Card Transactions in unlabeled data of bank Using Outlier Detection Techniqes
With the advancement of technology, the use of ATM and credit cards are increased. Cyber fraud and theft are the kinds of threat which result in using these Technologies. It is therefore inevitable to use fraud detection algorithms to prevent fraudulent use of bank cards. Credit card fraud can be thought of as a form of identity theft that consists of an unauthorized access to another person's ...
متن کاملNewsFerret: Supporting Identity Risk Identification and Analysis Through News Story Text Mining
Individuals, organizations, and devices are now interconnected to an unprecedented degree, forcing identity risk analysts to redefine “identity” in such contexts and explore new techniques for analyzing expanding threat contexts. Major hurdles to modeling in this field include a lack of publicly available data due to privacy and safety concerns, as well as the unstructured nature of incident re...
متن کاملBounded rationality of identity thieves: Using offender-based research to inform policy*
Research Summary Data for this study were collected in semistructured interviews with 59 individuals serving time in federal prisons for identity theft. We explore how offenders’ experiences and life circumstances affected their subjective assessments of risks and rewards and thus facilitated the decision to engage in identity theft. Our findings suggest that offenders perceive identity theft a...
متن کاملHttp Botnet Detection Using Frequent Patternset Mining
Among the diverse forms of malware, Botnet is the most widespread and serious threat which occurs commonly in today's cyberattacks. A botnet is a group of compromised computers which are remotely controlled by hackers to launch various network attacks, such as DDoS attack, spam, click fraud, identity theft and information phishing. The defining characteristic of botnets is the use of command an...
متن کامل