Detecting Phishing Websites Using Rule-Based Classification Algorithm: A Comparison
In today’s time, phishy website detection is one of the important challenges in the field of information security due to the large numbers of online transactions going through over the websites. Website phishing means stealing one’s personal information o
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Abstract In today’s time, phishy website detection is one of the important challenges in the field of information security due to the large numbers of online transactions going through over the websites. Website phishing means stealing one’s personal information over the Internet such as system backup data, user login credentials, bank account details or other security information. Phishing means creation of phishy or fake websites which look like legitimate ones. In this research paper, we use the associative classification data mining approach that is also named as rule-based classification technique by which we can detect a phishy website and thereby identifying the better detection algorithm which has a higher accuracy detection rate. The algorithms used are Naïve Bayes and PART algorithms of associative classification data mining approach. Moreover, we classify the websites into a legitimate website or a phishy website from the collected datasets of websites. The implementation will be done on the datasets of 1,353 websites which contain phishy sites as well as legitimate sites. At the end, results will show us the higher accuracy detection rate algorithm, which will more correctly identify phishing or legitimate websites. Keywords Phishy website
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Naïve Bayes algorithm
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PART algorithm
S. Gautam (✉) ⋅ K. Rani ⋅ B. Joshi Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India e-mail: [email protected] K. Rani e-mail: [email protected] B. Joshi e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 D.K. Mishra et al. (eds.), Information and Communication Technology for Sustainable Development, Lecture Notes in Networks and Systems 9, https://doi.org/10.1007/978-981-10-3932-4_3
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1 Introduction Website phishing is an Internet scam in which the user is unknown of the fact that the user is being targeted. A phisher targets the online user rather than the computer and shares his valuable information. Nowadays, website phishing has been the main security concern of online users due to the increased demand of using e-commerce websites. Here, we use associative classification (AC) in data mining which has a higher accuracy detection rate that may effectively detect phishy websites [1]. Phishing attacks are those in which victims are hacked by spoofed emails and fraud websites into giving up their personal information. Phishing will redirect the user to a different website through the user click within the website, email attachments, fake link, instant messages, spyware, etc. Also, the phishy attacker offers illegitimate websites to the user to fill up their personal information. Phisher mainly targets online users over the Internet. Associative classification is a method to define the classes from the trained datasets and classify the new class label in the predefined class or make new class groups. It is to get them trained from given train datasets and based on that gives the prediction results. The rule classi
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