CyberProtector:IDENTIFYING COMPROMISED URLs IN ELECTRONIC MAILS WITH BAYESIAN CLASSIFICATION
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Date
2016
Authors
AZEEZ, Nureni
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Abstract
Embedding malicious URLs in e-mails is one
of the most common web threats facing the internet
community today. Malicious URLs have been widely
used to mount various cyber-attacks like spear
phishing, pharming, phishing and malware. By falsely
claiming to be a trustworthy entity, users are lured into
clicking on these compromised links to divulge vital
information such as usernames, passwords, or credit
card details and unknowingly succumb to identity theft.
Hence, the detection of malicious URLs in e-mails is
very essential so as to help internet users implement
safe practices and as well prevent them from becoming
victims of fraud. This paper explores how malicious
links in e-mails can be detected from the lexical and
host-based features of their URLs to protect users from
identity theft attacks. This research uses Naïve Bayesian
classifier as a probabilistic model to detect if a URL is
malicious or legitimate. The Naïve Bayesian classifier is
used to count up the occurrence of each feature in an email
and calculate the cumulative score. If the
cumulative score is greater than the given threshold, the
URL is considered malicious otherwise the URL is
legitimate.
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Keywords
Malicious URLs; Pharming; Phishing, Attacks; Naïve Bayesian classifier; threshold