Indian Phishing Landscape: A Machine Learning and Deep Learning Approach for Detecting Malicious URLs and Curating an Indigenous Dataset
Keywords:
CNN, Cyber Security, Deep Learning, Indian Malicious Website Dataset, LSTM, Machine Learning, RNN, URL PhishingAbstract
Owing to the advancing technologies, activities like browsing the web for critical information, posting on social media and preference for online transactions have increased tremendously. At such a time, it is quite easy to fall into the trap of phishing websites that clone legitimate sites. The paper implements cutting-edge, remarkable Machine Learning techniques which uses Decision Tree algorithm along with Random Forest Classifier, SGD, Logistic Regression, K-Neighbors and Deep Learning methods like CNN, RNN and LSTM on two datasets to classify the online websites as ‘phishing’ and ‘benign’. One of the dataset used is curated to focus on Indian Phishing Website data thus following a more targeted approach in the detection of malicious websites and allowing a proactive defense against attackers. The paper slices and categorizes the data by URL length, top-level domain, symbol counting and other hyper-parameters for seamless feature extraction. The paper also uses TF-IDF for metrics generation as it overcomes limitations of the simple frequency counts and creates a distinctive categorization of the input data values. The fusion of ML and DL techniques to achieve robust cybersecurity, effective training and testing of data on authenticated datasets and a personalized curation of a dataset for top Indian Phishing Websites is how the paper bridges the gap between the research till date.
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