Event Details
Malicious URL Detection Using Machine Learning
Presenter: Abdul Aleem Syed
Supervisor:
Date: Mon, July 25, 2022
Time: 08:00:00 - 00:00:00
Place: ZOOM - Please see below.
ABSTRACT
Location: Remote via Zoom
https://uvic.zoom.us/j/85133750242?pwd=T3d0UDJoNDNpODB3TTVyWHNCcVVxdz09
Meeting ID: 851 3375 0242
Password: 706385
Note: Please log in to Zoom via SSO and your UVic Netlink ID.
Abstract:
The detection of malicious Uniform Resource Locators (URLs) is important for network and cybersecurity. The Internet has long been a platform for online criminal activity. In this project, supervised Machine Learning (ML) is applied to identify and detect malicious URLs. The ISCX- URL-2016 dataset from the Canadian Institute for Cyber Security is employed for evaluation. This dataset contains 79 features with four classes of URLs, namely spam, malware, phishing, and benign. The Waikato Environment for Knowledge Analysis (WEKA) tool is used to test and train the ML classifiers. To compare the results, k-cross-validation is used with k = 5 and k = 10. Principal Component Analysis (PCA) is employed for dimensionality reduction of the dataset and the important features are selected using eigenvalues. The ML classifiers evaluated are Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Bayesian Network (BayesNet), and Simple Logistic. The results obtained show that RF provides the best accuracy, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR).