Abstract
Spam emails have become a global menace since the rise of the Internet era. In fact, according to an estimate, around 50% of the emails are spam emails. Spam emails as part of a phishing scam can be sent to the masses with the motive to perform information stealing, identity theft, and other malicious actions. The previous studies showed that 91% of the cyber attacks start with the phishing emails, which contain Uniform Resource Locator (URLs). Although these URLs have several characteristics which make them distinguishable from the usual website links, yet a human eye cannot easily notice these URLs. Previous research also showed that traditional systems such as blacklisting/whitelisting of IPs and spam filters could not efficiently detect phishing and spam emails. However, Machine Learning (ML) approaches have shown promising results in combating spamming and phishing attacks. To identify these threats, we used several ML algorithms to train spam and phishing detector. The proposed framework is based on several linguistic and URL based features. Our proposed model can detect the spam and phishing emails with the accuracy of 89.2% and 97.7%, respectively.
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Kumar, S., Faizan, A., Viinikainen, A., Hamalainen, T. (2018). MLSPD - Machine Learning Based Spam and Phishing Detection. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_43
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