algoXSSF: Detection and analysis of cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks via Machine learning algorithms
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917581088096256 |
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| author | Kshetri, Naresh Kumar, Dilip Hutson, James Kaur, Navneet Osama, Omar Faruq |
| author_facet | Kshetri, Naresh Kumar, Dilip Hutson, James Kaur, Navneet Osama, Omar Faruq |
| contents | The global rise of online users and online devices has ultimately given rise to the global internet population apart from several cybercrimes and cyberattacks. The combination of emerging new technology and powerful algorithms (of Artificial Intelligence, Deep Learning, and Machine Learning) is needed to counter defense web security including attacks on several search engines and websites. The unprecedented increase rate of cybercrime and website attacks urged for new technology consideration to protect data and information online. There have been recent and continuous cyberattacks on websites, web domains with ongoing data breaches including - GitHub account hack, data leaks on Twitter, malware in WordPress plugins, vulnerability in Tomcat server to name just a few. We have investigated with an in-depth study apart from the detection and analysis of two major cyberattacks (although there are many more types): cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks. The easy identification of cyber trends and patterns with continuous improvement is possible within the edge of machine learning and AI algorithms. The use of machine learning algorithms would be extremely helpful to counter (apart from detection) the XSRF and XSS attacks. We have developed the algorithm and cyber defense framework - algoXSSF with machine learning algorithms embedded to combat malicious attacks (including Man-in-the-Middle attacks) on websites for detection and analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | algoXSSF: Detection and analysis of cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks via Machine learning algorithms Kshetri, Naresh Kumar, Dilip Hutson, James Kaur, Navneet Osama, Omar Faruq Cryptography and Security The global rise of online users and online devices has ultimately given rise to the global internet population apart from several cybercrimes and cyberattacks. The combination of emerging new technology and powerful algorithms (of Artificial Intelligence, Deep Learning, and Machine Learning) is needed to counter defense web security including attacks on several search engines and websites. The unprecedented increase rate of cybercrime and website attacks urged for new technology consideration to protect data and information online. There have been recent and continuous cyberattacks on websites, web domains with ongoing data breaches including - GitHub account hack, data leaks on Twitter, malware in WordPress plugins, vulnerability in Tomcat server to name just a few. We have investigated with an in-depth study apart from the detection and analysis of two major cyberattacks (although there are many more types): cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks. The easy identification of cyber trends and patterns with continuous improvement is possible within the edge of machine learning and AI algorithms. The use of machine learning algorithms would be extremely helpful to counter (apart from detection) the XSRF and XSS attacks. We have developed the algorithm and cyber defense framework - algoXSSF with machine learning algorithms embedded to combat malicious attacks (including Man-in-the-Middle attacks) on websites for detection and analysis. |
| title | algoXSSF: Detection and analysis of cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks via Machine learning algorithms |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2402.01012 |