| Title | Anti-phishing System using LSTM and CNN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | M, Yazhmozhi V., Janet, B., Reddy, Srinivasulu |
| Conference Name | 2020 IEEE International Conference for Innovation in Technology (INOCON) |
| Date Published | nov |
| Keywords | classification, CNN, Deep Learning, feature extraction, Human Behavior, phishing, pubcrawl, RNN, security, Support vector machines, Testing, Training, Uniform resource locators |
| Abstract | Users prefer to do e-banking and e-shopping now-a-days because of the exponential growth of the internet. Because of this paradigm shift, hackers are finding umpteen ways to steal our personal information and critical details like details of debit and credit cards, by disguising themselves as reputed websites, just by changing the spelling or making minor modifications to the URL. Identifying whether an URL is benign or malicious is a challenging job, because it makes use of the weakness of the user. While there are several works carried out to detect phishing websites, they only use heuristic methods and list based techniques and therefore couldn't avoid phishing effectively. In this paper an anti-phishing system was proposed to protect the users. It uses an ensemble model that uses both LSTM and CNN with a massive data set containing nearly 2,00,000 URLs, that is balanced. After analyzing the accuracy of different existing approaches, it has been found that the ensemble model that uses both LSTM and CNN performed better with an accuracy of 96% and the precision is 97% respectively which is far better than the existing solutions. |
| DOI | 10.1109/INOCON50539.2020.9298298 |
| Citation Key | m_anti-phishing_2020 |
Anti-phishing System using LSTM and CNN