Phishing Email Detection using Machine Learning
A V Santhosh Kumar1, Battula Deepthi2, A S Vamsy Krishna3,
Dadi Devaki4, Gandham Ebenezer John5
1Assistant Professor, Computer Science and Engineering, Raghu Engineering College, Visakhapatnam
[2-5]B.Tech Students, Computer Science and Engineering, Raghu Institute Of Technology, Visakhapatnam
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Abstract - The increasing sophistication of phishing attacks poses a significant threat to cybersecurity, demanding advanced and adaptive detection mechanisms. This project focuses on the development and implementation of a phishing detection system leveraging machine learning algorithms. The objective is to enhance the security measures for users by employing a robust and intelligent system capable of identifying and thwarting phishing attempts.
The project involves the collection and curation of diverse datasets containing examples of phishing and legitimate communication. Supervised learning techniques will be employed to train the machine learning model, utilizing features such as email content, sender information, and website attributes. The model's training will be continuously refined to adapt to emerging phishing tactics and enhance its accuracy. Additionally, unsupervised learning methods will be explored to detect novel phishing patterns and anomalies that may not be present in the training dataset. Clustering algorithms will assist in grouping similar instances, aiding the identification of previously unseen phishing techniques.
The implementation will be evaluated through extensive testing using real-world datasets, measuring the system's effectiveness in detecting phishing attempts with a low false-positive rate. The project aims to contribute to the field of cybersecurity by providing a proactive and adaptive solution to counter the evolving nature of phishing threats. The successful implementation of this phishing detection system will enhance user confidence in online interactions and contribute to the overall resilience of digital ecosystems.
Key Words: Phishing attacks, Cybersecurity, Detection mechanisms, Machine learning algorithms, Supervised learning, Unsupervised learning, Email content analysis, Sender information, Website attributes, Training dataset, Real-world datasets, False-positive rate, Proactive cybersecurity, Adaptive solutions, Digital ecosystems.
