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Malicious URLs are designed with the intention of promoting cyber attacks such as spam, phishing, and malware to exploit/deface users’ information and resources. These attacks are transmitted via emails, text messages, pop-ups, and devious advertisements. Therefore, detecting these threats at the time of clicking is crucial for enhancing cybersecurity.
There are a number of strategies for detecting malicious URLs, ranging from feature-based to blacklist-based detection. Feature-based detection involves identifying and examining features that represent the URL, while blacklist-based detection relies on the use of heuristics such as TLD substitution, IP address equivalence, query string substitution, directory structure similarity, brand name equivalence, and others.
Digital Defenders: Exploring the World of Bulk Malicious URL Scanning
Using machine learning to identify malicious URLs is an appealing approach because it enables the application of more complex algorithms, which are able to detect a wider range of patterns. However, the training of these models can be very computationally intensive and therefore, require a large amount of data to produce adequate results.
To address this issue, instance selection methods were used to generate three smaller but representative datasets, expediting model training and facilitating the identification of critical features for classifying malicious URLs. Ultimately, the resulting model was able to classify malicious URLs with high accuracy and efficiency. Moreover, the performance of the model was further enhanced by using Bayesian optimization for hyperparameter tuning.