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Phishguard: ML- based phishing detection system

Authors:

Bhagyashri  Satarkar^*1, Dhavalsigh Vibhute^*2, Vishal Wadgoankar^*3, Yasar Sayyad^*4

Under the Guidance of Prof. PradnyaKothawade

Genba Moze College of Engineering,Balewadi

Abstract: Spam messages in SMS and email systems pose significant security and productivity risks. Traditional detection methods, such as rule-based filters, struggle to adapt to evolving spam techniques. This paper explores machine learning techniques for spam detection, emphasizing algorithms like Naïve Bayes and Support Vector Machines (SVM), along with their applications, strengths, and limitations. Key challenges, including scalability, dataset diversity, and evolving spam patterns, are identified. The study highlights future directions such as real-time classification, deep learning models, and improved feature engineering to enhance adaptive and robust spam detection systems..

Keywords: Spam Detection, Machine Learning, Text Classification, Naive Bayes, Feature Engineering, Cybersecurity