ABSTRACT Safe web browsing and feeding confidential information into websites require the use of protected and secured websites. For the web security, a number of anti-phishing tools have been proposed which provide web user with a dynamic system of warning and protection against potential phishing attacks. An earlier study shows that there is no anti-phishing tool gives satisfactory result in identifying the phishing web pages. For the solution of this problem, in this paper, a Bayesian classification approach is proposed to identify the phishing websites. Bayesian filter requires two datasets in their approach; one is legitimate website details and the second thing is phishing website parameters. A large set of legitimate transactional websites there needed in the study because the set of websites mostly resembles just like phishing websites and the filter must have numerous examples of legitimate transactional websites to achieve a low false positive rate. With the use of Bayesian Classification, some prominent results obtained by selecting phishing indicators.
KEYWORDS: Phishing and Anti-Phishing, Legitimate Web page, Phishing Web page, Bayesian Classifier
INTRODUCTION The term Phishing is emerged for spoofing websites which are used for stealing confidential information of the web user such as banking passwords, credit card credential and user’s private information on the web. An unaware user about phishing, inter the confidential information in such type of websites and get lost their information. The research on the topic ‘Phishing’ is being continuing because of different phishing attacks are generating day-by-day with different techniques and software use. The status of the legitimate and phishing websites which are identified by the APWG in the third quarter of the 2016 is as given below in Table 1.
A number of techniques can be used to deceive the user and alter the user’s data by spreading virus, malwares, worms etc. Apart from the spam attack, the cyber criminals are turning to the social networks to launch their phishing website attack. The varying nature of attacks, the network user incorrectly assuming that they are not at risky condition. The attacker takes benefit by using these sites to target new victims. The goal of this research study is to analyze the previously defined anti-phishing systems, its performance effectiveness and to provide the best possible solution to countermeasure the phishing attack.
KEYWORDS: Phishing and Anti-Phishing, Legitimate Web page, Phishing Web page, Bayesian Classifier
INTRODUCTION The term Phishing is emerged for spoofing websites which are used for stealing confidential information of the web user such as banking passwords, credit card credential and user’s private information on the web. An unaware user about phishing, inter the confidential information in such type of websites and get lost their information. The research on the topic ‘Phishing’ is being continuing because of different phishing attacks are generating day-by-day with different techniques and software use. The status of the legitimate and phishing websites which are identified by the APWG in the third quarter of the 2016 is as given below in Table 1.
A number of techniques can be used to deceive the user and alter the user’s data by spreading virus, malwares, worms etc. Apart from the spam attack, the cyber criminals are turning to the social networks to launch their phishing website attack. The varying nature of attacks, the network user incorrectly assuming that they are not at risky condition. The attacker takes benefit by using these sites to target new victims. The goal of this research study is to analyze the previously defined anti-phishing systems, its performance effectiveness and to provide the best possible solution to countermeasure the phishing attack.
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