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Publication Type : Conference Paper
Publisher : 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018
Source : 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018, Institute of Electrical and Electronics Engineers Inc. (2018)
ISBN : 9781538644300
Keywords : Anti-phishing working groups, Brain, Computer crime, Convolution, Convolution neural network, Convolutional neural network, Convolutional Neural Networks (CNN), Deep learning, information dissemination, Learning algorithms, Learning systems, Logistic regressions, Long short-term memory, Machine learning techniques, Phishing, Privacy and security, Websites
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Computer Science
Year : 2018
Abstract : In the modern epoch, all information is easily accessible through websites and due to this reason people rely completely on online resources. On the contrary to its advantages, privacy and security in online media are the main concern worldwide because of the rise in phishing attacks launched online. The number of phishing websites increases every month targeting more than 450 brands, as per the reports published by anti-phishing working groups(APWG). Traditionally blacklists are used to detect the URL attacks. But with the exponential increase in the number of phishing websites, this method has its own limitations and it also fails to detect newly generated phishing URLs which can be solved using machine learning or deep learning techniques. Here we present a comparative study between classical machine learning technique - logistic regression using bigram, deep learning techniques like convolution neural network(CNN) and CNN long short-term memory(CNN-LSTM) as architectures used to detect malicious URLs. On comparison CNN-LSTM gave the best accuracy of about 98% for the classification of phishing URLs.
Cite this Research Publication : A. Vazhayil, Vinayakumar, R., and Dr. Soman K. P., “Comparative Study of the Detection of Malicious URLs Using Shallow and Deep Networks”, in 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018, 2018.