Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Cantina: a content-based approach to detecting phishing web sites
Proceedings of the 16th international conference on World Wide Web
A hybrid phish detection approach by identity discovery and keywords retrieval
Proceedings of the 18th international conference on World wide web
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Using Domain Top-page Similarity Feature in Machine Learning-Based Web Phishing Detection
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Detecting visually similar Web pages: Application to phishing detection
ACM Transactions on Internet Technology (TOIT)
Expert Systems with Applications: An International Journal
Intelligent phishing detection system for e-banking using fuzzy data mining
Expert Systems with Applications: An International Journal
PhishTester: Automatic Testing of Phishing Attacks
SSIRI '10 Proceedings of the 2010 Fourth International Conference on Secure Software Integration and Reliability Improvement
Automatic Detection of Phishing Target from Phishing Webpage
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites
ACM Transactions on Information and System Security (TISSEC)
PhishZoo: Detecting Phishing Websites by Looking at Them
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
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Phishing is an instance of social engineering techniques used to deceive users into giving their sensitive information using an illegitimate website that looks and feels exactly like the target organization website. Most phishing detection approaches utilizes Uniform Resource Locator (URL) blacklists or phishing website features combined with machine learning techniques to combat phishing. Despite the existing approaches that utilize URL blacklists, they cannot generalize well with new phishing attacks due to human weakness in verifying blacklists, while the existing feature-based methods suffer high false positive rates and insufficient phishing features. As a result, this leads to an inadequacy in the online transactions. To solve this problem robustly, the proposed study introduces new inputs (Legitimate site rules, User-behavior profile, PhishTank, User-specific sites, Pop-Ups from emails) which were not considered previously in a single protection platform. The idea is to utilize a Neuro-Fuzzy Scheme with 5 inputs to detect phishing sites with high accuracy in real-time. In this study, 2-Fold cross-validation is applied for training and testing the proposed model. A total of 288 features with 5 inputs were used and has so far achieved the best performance as compared to all previously reported results in the field.