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Authors: Rakesh M. Verma and Nirmala Rai

Affiliation: University of Houston, United States

Keyword(s): Phishing, Message-ID, N-gram, Machine Learning.

Related Ontology Subjects/Areas/Topics: Information and Systems Security ; Network Security ; Security and Privacy in Web Services ; Wireless Network Security

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.

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Paper citation in several formats:
M. Verma, R. and Rai, N. (2015). Phish-IDetector: Message-Id Based Automatic Phishing Detection. In Proceedings of the 12th International Conference on Security and Cryptography (ICETE 2015) - SECRYPT; ISBN 978-989-758-117-5; ISSN 2184-3236, SciTePress, pages 427-434. DOI: 10.5220/0005574304270434

@conference{secrypt15,
author={Rakesh {M. Verma}. and Nirmala Rai.},
title={Phish-IDetector: Message-Id Based Automatic Phishing Detection},
booktitle={Proceedings of the 12th International Conference on Security and Cryptography (ICETE 2015) - SECRYPT},
year={2015},
pages={427-434},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005574304270434},
isbn={978-989-758-117-5},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Security and Cryptography (ICETE 2015) - SECRYPT
TI - Phish-IDetector: Message-Id Based Automatic Phishing Detection
SN - 978-989-758-117-5
IS - 2184-3236
AU - M. Verma, R.
AU - Rai, N.
PY - 2015
SP - 427
EP - 434
DO - 10.5220/0005574304270434
PB - SciTePress