Event Details
Malicious URL Detection using Machine Learning
Presenter: Abubakar Siddeeq
Supervisor:
Date: Thu, October 13, 2022
Time: 09:00:00 - 10:00:00
Place: ZOOM - Please see below.
ABSTRACT
Zoom Meeting Link: https://uvic.zoom.us/j/8645337187?pwd=UnZsKy9rbitTM1ZLUnA5d1JicGhQdz09
Meeting ID: 864 533 7187
Password: 999511
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Abstract:
Malicious URL detection is important for cyber security experts and security agencies. With the drastic increase in internet usage, the distribution of such malware is a serious issue. Due to the wide variety of this malware, detection even with antivirus software is difficult. More than 12.8 million malicious URL websites are currently running. In this thesis, several machine learning classifiers along with ensemble methods are used to formulate a framework to detect this malware. Principal component analysis, k-fold cross-validation, and hyperparameter tuning are used to improve performance. A dataset from Kaggle is used for classification. Accuracy, precision, recall, and f-score are used as metrics to determine the model performance. Moreover, model behavior with a majority of one label in the dataset is also examined as is typical in the real world.