Presenter(s)

    Bharath Srinivasan, Karthi Balasundaram, Mukilan Ashokraj Rajapriya

    Comments

    Presentation: 1:15-2:30 p.m., Kennedy Union Ballroom

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    Description

    Social Engineering is the process of deceiving people to reveal confidential information about themselves. There are various categories of Social Engineering, among which Phishing is the most frequently used attack nowadays; a new phishing site is created on the internet every 20 seconds and more than seventy percent of phishing emails are opened by their targets. From fraudulent emails to deploying malicious softwares on people's computers, phishing has become one of the main concerns that bothers the common people. There are various types of phishing such as Vishing (voice phishing), Email phishing, Smishing (SMS phishing) and many more, out of which we are going to deal with the email phishing. Email phishing is the practice of getting emails with malicious intents. The initial effort involved simulating potential phishing attacks within a controlled setup leading to devising a solution on how to detect and prevent clicking on the malicious links by common netizens like us. The developed Machine Learning (ML) model was able to classify the vulnerable links with 97.88% training and 96.4% testing accuracies respectively. Overall, the work provides a comprehensive overview of the state-of-the-art in ML based phishing email detection, and highlights the potential of ML techniques to enhance the security of individuals and organizations against phishing attacks. Keywords : Social Engineering, Natural Language Processing, Sentimental analysis, Email Scams, CyberSecurity Automation, Individuals, Organizations

    Publication Date

    4-19-2023

    Project Designation

    Course Project 202280 CPS 574 01

    Primary Advisor

    Phu Phung

    Primary Advisor's Department

    Computer Science

    Keywords

    Stander Symposium, College of Arts and Sciences

    Institutional Learning Goals

    Practical Wisdom; Community

    Phishing-Attack, Detection and Prevention

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