Detection of Fraudulent Emails by Authorship Extraction
نویسندگان
چکیده
Fraudulent emails can be detected by extraction of authorship information from the contents of emails. This paper presents information extraction based on unique words from the emails. These unique words will be used as representative features to train Radial Basis function (RBF). Final weights are obtained and subsequently used for testing. The percentage of identification of email authorship depends upon number of RBF centers and the type of functional words used for training RBF. One hundred and fifty authors with over one hundred files from the sent folder of Enron email dataset are considered. A total of 300 unique words of number of characters in each word ranging from three to seven are considered. Training and testing of RBF are done by taking different lengths of words. Our simulation shows the effectiveness of the proposed RBF network for email authorship identification. The accuracy of authorship identification ranges from 95% to 97%.
منابع مشابه
Anatomy of a Phishing Email
This paper discusses the tricks employed by email scammers in “phishing” emails, which are emails that spoof a reputable company in an attempt to defraud the recipient of personal information. These tricks are classified according to whether they are employed in the fraudulent emails or used in the fraudulent Web pages accessed by a link provided in the email. All of the examples used within th...
متن کاملDetection Phishing Emails Using Features Decisive Values
Phishing emails are messages designed to fool the recipient into handing over personal information, such as login names, passwords, credit card numbers, account credentials, social security numbers etc. Fraudulent emails harm their victims through loss of funds and identity theft. They also hurt Internet business, because people lose their trust in Internet transactions for fear that they will ...
متن کاملPresenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm
both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, ...
متن کاملDetection of Phishing Emails using Feed Forward Neural Network
Phishing emails are messages designed to fool the recipient into handing over personal information, such as login names, passwords, credit card numbers, account credentials, social security numbers etc. Fraudulent emails harm their victims through loss of funds and identity theft. They also hurt Internet business, because people lose their trust in Internet transactions for fear that they will ...
متن کاملMEFUASN: A Helpful Method to Extract Features using Analyzing Social Network for Fraud Detection
Fraud detection is one of the ways to cope with damages associated with fraudulent activities that have become common due to the rapid development of the Internet and electronic business. There is a need to propose methods to detect fraud accurately and fast. To achieve to accuracy, fraud detection methods need to consider both kind of features, features based on user level and features based o...
متن کامل