Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning

Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or creat...

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Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 9; p. 3373
Main Authors:	Alsaedi, Mohammed, Ghaleb, Fuad, Saeed, Faisal, Ahmad, Jawad, Alasli, Mohammed
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 28.04.2022 MDPI
Subjects:	Algorithms Analysis Computational linguistics Computer hackers Computer Security cyber threat intelligence cybersecurity Cyberterrorism Database searching Decision making ensemble learning Identity theft Intelligence Internet of Things internet security Internet software Internet/Web search services Language processing Machine Learning malicious URLs Natural language interfaces Neural Networks, Computer Online searching Phishing Spyware Technology application URLs Web applications Websites ensemble learning cyber threat intelligence malicious URLs internet security cybersecurity
ISSN:	1424-8220, 1424-8220
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Abstract	Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website’s content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies’ detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.
AbstractList	Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website’s content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies’ detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model. Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website's content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies' detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website's content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies' detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.
Audience	Academic
Author	Alsaedi, Mohammed Saeed, Faisal Alasli, Mohammed Ghaleb, Fuad Ahmad, Jawad
AuthorAffiliation	3 DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK 4 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK; j.ahmad@napier.ac.uk 1 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia 2 College of Computer Science and Engineering, Taibah University, P.O. Box 344, Medina 41411, Saudi Arabia; masadi@taibahu.edu.sa (M.A.); fsaeed@taibahu.edu.sa (F.S.); masali@taibahu.edu.sa (M.A.)
AuthorAffiliation_xml	– name: 3 DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK – name: 4 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK; j.ahmad@napier.ac.uk – name: 2 College of Computer Science and Engineering, Taibah University, P.O. Box 344, Medina 41411, Saudi Arabia; masadi@taibahu.edu.sa (M.A.); fsaeed@taibahu.edu.sa (F.S.); masali@taibahu.edu.sa (M.A.) – name: 1 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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SubjectTerms	Algorithms Analysis Computational linguistics Computer hackers Computer Security cyber threat intelligence cybersecurity Cyberterrorism Database searching Decision making ensemble learning Identity theft Intelligence Internet of Things internet security Internet software Internet/Web search services Language processing Machine Learning malicious URLs Natural language interfaces Neural Networks, Computer Online searching Phishing Spyware Technology application URLs Web applications Websites
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Title	Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning
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