DocumentCode :
3781565
Title :
Phish-IDetector: Message-ID based automatic phishing detection
Author :
Rakesh Verma;Nirmala Rai
Author_Institution :
Department of Computer Science, University of Houston, 4800 Calhoun Road, Texas, U.S.A.
Volume :
4
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
427
Lastpage :
434
Abstract :
Phishing attacks are a well known problem in our age of electronic communication. Sensitive information like credit card details, login credentials for account, etc. are targeted by phishers. Emails are the most common channel for launching phishing attacks. They are made to resemble genuine ones as much as possible to fool recipients into divulging private and sensitive data, causing huge monetary losses every year. This paper presents a novel approach to detect phishing emails, which is simple and effective. It leverages the unique characteristics of the Message-ID field of an email header for successful detection and differentiation of phishing emails from legitimate ones. Using machine learning classifiers on n-gram features extracted from Message-IDs, we obtain over 99% detection rate with low false positives.
Keywords :
"Electronic mail","Machine learning algorithms","Prediction algorithms","Postal services","Decision trees","Feature extraction","Algorithm design and analysis"
Publisher :
ieee
Conference_Titel :
e-Business and Telecommunications (ICETE), 2015 12th International Joint Conference on
Type :
conf
Filename :
7518067
Link To Document :
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