Deep Learning Based-Phishing Attack Detection
K. Sumathi1, V. Sujatha2

1K Sumathi, Department of Computer Applications, CMS College of Science and Commerce, Coimbatore, (Tamil Nadu), India.
2V. Sujatha, Department of Computer Applications, CMS College of Science and Commerce, Coimbatore, (Tamil Nadu), India. 

Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 8428-8432 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6527098319/2019©BEIESP | DOI: 10.35940/ijrte.C6527.098319

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Abstract: Due to the rapid development of the communication technologies and global networking, lots of daily human life activities such as electronic banking, social networks, e-commerce, etc are transferred to the cyberspace. The anonymous, open and uncontrolled infrastructure of the internet enables an excellent platform for cyber attacks. Phishing is one of the cyber attacks in which attackers open some fraudulent websites similar to the popular and legal websites to steal the user’s sensitive information. Machine learning techniques such as J48, Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were widely to detect the phishing attacks. But, getting good-quality training data is one of the biggest problems in machine learning. So, a deep learning method called Deep Neural Network (DNN) is introduced to detect the phishing Uniform Resource Locators (URLs). Initially, a feature extractor is used to construct a 30-dimension feature vector based on URL-based features, HTML-based features and domain-based features. These features are given as input to the DNN classifier for phishing attack detection. It consists of one input layer, multiple hidden layers and one output layer. The multiple hidden layers in DNN try to learn high-level features in an incremental manner. Finally, the DNN returns a probability value which represent the phishing URLs and legitimate URLs. By using DNN the accuracy, precision and recall of phishing attack detection is improved.
Keywords: Artificial Neural Network, Deep Learning, Deep Neural Network, Phishing, Machine Learning.

Scope of the Article: Machine Learning.