Abstract
A malicious URL is a link that is created to spread spams, phishing, malware, ransomware, spyware, etc. A user may download malware that can adversely affect the computer by clicking on an infected URL, or might be convinced to provide confidential information to a fraudulent website causing serious losses. These threats must be identified and handled in a decent time and in an effective way. Detection is traditionally done through the blacklist usage method, which relies on keyword matching with previously known malicious domain names stored in a repository. This method is fast and easy to implement, with the advantage of having low false-positive rates regarding previously recognized malicious URLs. However, this method cannot recognize newly created malicious URLs. To solve this problem, many machine-learning models have been used. In this paper, we introduce an effective machine learning approach that uses an ensemble learner algorithm called AdaBoost (Adaptive Boosting), combined with different algorithms that enhance detection. For datasets filtration, we used CfsSubsetEval technique, which is an algorithm that searches for a subset of features that work well together. Datasets were collected from the UNB repository; divided into four categories: spam, phishing, malware, and defacement URLs; combined with benign URLs, dataset content is based on lexical features. The experimental results indicate that the proposed approach was successful in enhancing the detection accuracy of malicious URLs with less false-positive rates for all experimental algorithms.
Highlights
We introduce an effective machine learning approach that uses an ensemble learner algorithm called AdaBoost (Adaptive Boosting), combined with different algorithms that enhance detection
The results summarized in Tab. 6 show the precision, recall and accuracy performance of the Support Vector Machines (SVM) classifier on all datasets, before and after applying AdaBoost
We have discussed in this paper the challenges of detecting malicious URLs content using traditional methods, and how machine learning helped to address these challenges by providing effective models that capture a larger distribution of malicious URLs
Summary
A user could be manipulated to provide sensitive information to a phishing webpage voluntarily, or become a victim of a drive-by-download, ending with a malware infection [3,4]. Various types of malicious URLs exist, the most popular are phishing, spam, malware (drive-by download), and defacement URLs. Phishing websites are sites that seek to steal users' private and sensitive information, such as bank card numbers, or user credentials. Phishing websites are sites that seek to steal users' private and sensitive information, such as bank card numbers, or user credentials This is usually done by deceiving the users into thinking they are on a legitimate website.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
