Earticle

초록

영어

Email spoofing represents a common cybersecurity risk that abuses the weaknesses in the email protocols to falsify the sender addresses and trick the recipients into providing sensitive information or performing malicious requests. Conventional rulebased detection systems have been found ineffective against more advanced forms of spoofing. This research project suggests using machine learning to identify email spoofing using a range of features derived through email header, content, and metadata. Various algorithms, such as Support Vector Machines (SVM), Random Forest, and Logistic Regression, are tested to find the most suitable in terms of identifying legitimate emails and spoofed ones. On the dataset, preprocessing is done through tokenization, feature encoding, and vectorization to increase the model accuracy. The evaluation of the performance is performed in terms of precision, recall, F1-score, or ROC-AUC. Through experiments, it has been shown that machine learning models, especially ensemble based methods, greatly exceed traditional methods in accuracy and low false positive rate in detecting spoofed emails. This work has demonstrated the promise of intelligent systems when used to reinforce email security and has also offered a scalable implementation of real-time detection of spoofing.

목차

Abstract
I. INTRODUCTION
II. KEY CONTRIBUTIONS OF THIS WORK INCLUDE
III. RELATED WORK
IV. CONCULSION
REFERENCES

저자

• Khushbu Khalid Butt [ Department of Information Technology Lahore Garrison University Lahore, Pakistan ]
• Muhammad Yousaf [ Department of Computer Science Lahore Garrison University Lahore, Pakistan ]
• Khola Farooq [ Department of Computer Science Lahore Garrison University Lahore, Pakistan ]
• Rehan Malik [ Department of Computer Science Comsats Lahore Lahore, Pakistan ]
• Maria Tariq [ Department of Computer Science Lahore Garrison University Lahore, Pakistan ]
• Sundus Munir [ Department of Criminology Lahore Garrison University Lahore, Pakistan ]

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