Abstract:
Phishing attack is a simplest way to obtain sensitive information from innocent
users. Aim of the phishers is to acquire critical information like username, password
and bank account details. Cyber security persons are now looking for trustworthy
and steady detection techniques for phishing websites detection. This paper deals
with machine learning technology for detection of phishing URLs by extracting and
analyzing various features of legitimate and phishing URLs. Decision Tree, random
forest and Support vector machine algorithms are used to detect phishing websites.
Aim of the paper is to detect phishing URLs as well as narrow down to best machine
learning algorithm by comparing accuracy rate, false positive and false negative rate
of each algorithm.
Nowadays Phishing becomes a main area of concern for security researchers
because it is not difficult to create the fake website which looks so close to
legitimate website. Experts can identify fake websites but not all the users can
identify the fake website and such users become the victim of phishing attack. Main
aim of the attacker is to steal banks account credentials. In United States businesses,
there is a loss of US$2billion per year because their clients become victim to
phishing . In 3rd Microsoft Computing Safer Index Report released in February
2014, it was estimated that the annual worldwide impact of phishing could be as
high as $5 billion . Phishing attacks are becoming successful because lack of user
awareness. Since phishing attack exploits the weaknesses found in users, it is very
difficult to mitigate them but it is very important to enhance phishing detection
techniques.
The general method to detect phishing websites by updating blacklisted URLs,
Internet Protocol (IP) to the antivirus database which is also known as “blacklist"
method. To evade blacklists attackers uses creative techniques to fool users by
modifying the URL to appear legitimate via obfuscation and many other simple
techniques including: fast-flux, in which proxies are automatically generated to host
the web-page; algorithmic generation of new URLs; etc.
