Employing Gradient Boosting-Steepest Descent Approach for Identifying Potential Phishing Attacks

Employing Gradient Boosting-Steepest Descent Approach for Identifying Potential Phishing Attacks

Narendra Kumar Bairagi1, Prof. Vipin Kasera2

Research Scholar1, Asst. Professor2

VITM, Indore, India1,2

Abstract: Phishing attacks are among the most common cyber threats, tricking users into revealing sensitive information such as passwords, credit card details, and personal data. Traditional security measures, such as rule-based detection and blacklists, struggle to keep up with the evolving tactics of cybercriminals. Phishing attacks typically occur through emails, fake websites, or fraudulent messages that appear legitimate. Attackers use social engineering techniques to manipulate users into clicking malicious links or downloading harmful attachments. Due to the dynamic nature of phishing strategies, static detection methods often fail to provide adequate protection. Advanced machine learning techniques, particularly deep learning, have proven to be more effective in identifying complex patterns in phishing attempts. This paper presents a Gradient Boosting-Steepest Descent Approach for identifying potential phishing attacks. It has been shown that the proposed approach outperforms exiting baseline models in terms of classification accuracy.

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Keywords: Cyber Security,  Phishing Attacks, Gradient Boosting, Steepest Descent, Deep Neural Networks,  Classification Accuracy.