Hybrid Deep Learning Approach for Phishing Attack Detection |
Author(s): |
| Gauri Mahale , Amrutvahini college of engineering Sangamner, India ; Rutuja Kawar, Amrutvahini college of engineering Sangamner, India; Kirti Dhanapune, Amrutvahini college of engineering Sangamner, India; Sanjay Waghmode, Amrutvahini college of engineering Sangamner, India |
Keywords: |
| Machine Learning, Deep Learning, Hybrid Approach, Social Engineering, Online Security, Cybercrime, Internet Fraud, Classifier, Algorithms |
Abstract |
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Phishing attacks are a growing concern in today's digital age, and detecting them is a critical challenge for security researchers. A hybrid approach for phishing attack detection using machine learning and deep learning-based algorithms has been proposed to enhance the effectiveness of existing solutions. This approach combines the strengths of Support Vector Machines (SVM), Gaussian Naive Bayes (GNB), Random Forest (RF), Extreme Learning Machine (ELM), Multi-layer Perceptron (MLP), Gradient Boosting Classifier (GBC), and eXtreme Gradient Boosting (XGB) to achieve higher accuracy and reduce false positives. The proposed approach achieves an accuracy of 93.68% in detecting phishing attacks. However, new and improved methods are necessary to keep up with the evolving techniques used by hackers. |
Other Details |
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Paper ID: IJSRDV11I40060 Published in: Volume : 11, Issue : 4 Publication Date: 01/07/2023 Page(s): 45-54 |
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