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Title Real-Time Phishing URL Detection Using Machine Learning Techniques

Author(s) Saad Ul Haq

Abstract Real-time phishing Uniform Resources Locator URL detection is important due to the growing threat of phishing attacks on individuals and businesses. These attacks seek usernames, passwords, and credit card numbers. Fake emails and websites enable these attacks. The consequences can include money loss, identity theft, and reputation damage. Real-time phishing URL detection systems that use machine learning can reduce these risks. These technologies detect phishing websites by analyzing URLs and content. They quickly block these websites to prevent harm. This method helps adapt to evolving phishing attacks. In this research, we proposed a hybrid model that uses convolutional neural network CNN and long short-term memory networks LSTM. CNNs are used to predict phishing URL attacks using several feature engineering methodologies, while LSTM works on classification. This technology produces a more precise model than previous methods. Precision reduces false positives, preventing genuine websites from being misinterpreted for phishing attacks. The research effectively addresses phishing attacks by implementing a real-time detection system that boosts security and mitigates cyber dangers.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2024-11-20

Subject Electrical Engneering

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