Abstract
Phishing is one of the biggest crimes in the world and involves the theft of the user’s sensitive data. Usually, phishing websites target individuals’ websites, organizations, sites for cloud storage, and government websites. Most users, while surfing the internet, are unaware of phishing attacks. Many existing phishing approaches have failed in providing a useful way to the issues facing e-mails attacks. Currently, hardware-based phishing approaches are used to face software attacks. Due to the rise in these kinds of problems, the proposed work focused on a three-stage phishing series attack for precisely detecting the problems in a content-based manner as a phishing attack mechanism. There were three input values—uniform resource locators and traffic and web content based on features of a phishing attack and non-attack of phishing website technique features. To implement the proposed phishing attack mechanism, a dataset is collected from recent phishing cases. It was found that real phishing cases give a higher accuracy on both zero-day phishing attacks and in phishing attack detection. Three different classifiers were used to determine classification accuracy in detecting phishing, resulting in a classification accuracy of 95.18%, 85.45%, and 78.89%, for NN, SVM, and RF, respectively. The results suggest that a machine learning approach is best for detecting phishing.
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Reference33 articles.
1. phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training
2. Decisive Heuristics to Differentiate Legitimate from Phishing Sites;Sophie;Proceedings of the Network and Information Systems Security (SAR-SSI),2011
3. New rule-based phishing detection method
4. Phishnet: Predictive Blacklisting to Detect Phishing Attacks;Prakash;Proceedings of the 2010 IEEE INFOCOM,2010
5. Sender ID: Authenticating E-mail;Jim;RFC 4406,2006
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献