Student Name: SM Baig
About the student
I am Shawal Baig, a 23-year-old pursuing a Computer Science Honors degree at the University of Johannesburg, having completed my BSc in IT at the same institution. My primary academic interests lie in Artificial Intelligence and Cybersecurity. Outside of academics, I am passionate about cricket and badminton, enjoy playing the keyboard, and appreciate spending time outdoors.
About the Project
My project focuses on improving online safety by using machine learning to tell apart phishing URLs from safe ones. Phishing URLs try to trick users into giving up personal details like passwords or bank information. I added three machine learning models to an Android app to help with this: Support Vector Machine (SVM), Random Forest, and Linear Regression. I used a dataset from Kaggle, which has examples of both types of URLs, to train these models. The SVM and Random Forest models did the best job telling the URLs apart. SVM separates data really well, and Random Forest uses many decision trees to get accurate results. Linear Regression wasn't as effective for this job. My Android app makes things easy for users. They just enter a URL, and the app tells them if it's safe or a possible phishing attempt. This helps users stay safe online. To wrap up, my project uses machine learning in an Android app to spot phishing URLs. Using the Kaggle dataset with SVM and Random Forest models makes the web a safer place." My project focuses on improving online safety by using machine learning to tell apart phishing URLs from safe ones. Phishing URLs try to trick users into giving up personal details like passwords or bank information. I added three machine learning models to an Android app to help with this: Support Vector Machine (SVM), Random Forest, and Linear Regression. I used a dataset from Kaggle, which has examples of both types of URLs, to train these models. The SVM and Random Forest models did the best job telling the URLs apart. SVM separates data really well, and Random Forest uses many decision trees to get accurate results. Linear Regression wasn't as effective for this job. My Android app makes things easy for users. They just enter a URL, and the app tells them if it's safe or a possible phishing attempt. This helps users stay safe online. To wrap up, my project uses machine learning in an Android app to spot phishing URLs. Using the Kaggle dataset with SVM and Random Forest models makes the web a safer place.
