Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications
The rapid progress of technological developments in the global world, the people to closely follow these developments and share them have become the focus of cybercriminals. People realize their basic needs, requests, shares or works via smart devices using the internet infrastructure. While perform...
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| Format: | Article |
| Language: | English |
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Gazi University
2021-12-01
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| Series: | Gazi Üniversitesi Fen Bilimleri Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1817405 |
| _version_ | 1797906387709198336 |
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| author | Mesut TOĞAÇAR |
| author_facet | Mesut TOĞAÇAR |
| author_sort | Mesut TOĞAÇAR |
| collection | DOAJ |
| description | The rapid progress of technological developments in the global world, the people to closely follow these developments and share them have become the focus of cybercriminals. People realize their basic needs, requests, shares or works via smart devices using the internet infrastructure. While performing these actions, users can inevitably leave an open door through web applications. As a result, user-defined information can easily be passed on to others. Recently, there has been a serious increase in activities carried out on websites. One of the reasons for this increase, and the most important one, is the pandemic that has had an impact worldwide. Cybercriminals want to turn such situations into opportunities and gain financial gain. They look for vulnerabilities in the websites that people demand heavily and they want to access their user information and card information. This study proposes an approach that measures the performance of machine learning methods against the vulnerabilities of various websites. The data set used in the study consists of parameter properties of 1000 websites. In the experimental analysis of the study; Multilayer Perceptron, Support Vector Machines, Decision Trees, Naive Bayesian, Random Forest methods were used. The general accuracy achievements obtained from machine learning methods are; it was 74%, 73.7%, 100%, 69.5% and 100%, respectively. Experimental analysis has shown that machine learning methods are effective in detecting cyber attacks. |
| first_indexed | 2024-04-10T10:20:08Z |
| format | Article |
| id | doaj.art-7cf1d52109744800b7e530485419a09c |
| institution | Directory Open Access Journal |
| issn | 2147-9526 |
| language | English |
| last_indexed | 2024-04-10T10:20:08Z |
| publishDate | 2021-12-01 |
| publisher | Gazi University |
| record_format | Article |
| series | Gazi Üniversitesi Fen Bilimleri Dergisi |
| spelling | doaj.art-7cf1d52109744800b7e530485419a09c2023-02-15T16:21:39ZengGazi UniversityGazi Üniversitesi Fen Bilimleri Dergisi2147-95262021-12-019460862010.29109/gujsc.950639Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web ApplicationsMesut TOĞAÇARhttps://orcid.org/0000-0002-8264-3899The rapid progress of technological developments in the global world, the people to closely follow these developments and share them have become the focus of cybercriminals. People realize their basic needs, requests, shares or works via smart devices using the internet infrastructure. While performing these actions, users can inevitably leave an open door through web applications. As a result, user-defined information can easily be passed on to others. Recently, there has been a serious increase in activities carried out on websites. One of the reasons for this increase, and the most important one, is the pandemic that has had an impact worldwide. Cybercriminals want to turn such situations into opportunities and gain financial gain. They look for vulnerabilities in the websites that people demand heavily and they want to access their user information and card information. This study proposes an approach that measures the performance of machine learning methods against the vulnerabilities of various websites. The data set used in the study consists of parameter properties of 1000 websites. In the experimental analysis of the study; Multilayer Perceptron, Support Vector Machines, Decision Trees, Naive Bayesian, Random Forest methods were used. The general accuracy achievements obtained from machine learning methods are; it was 74%, 73.7%, 100%, 69.5% and 100%, respectively. Experimental analysis has shown that machine learning methods are effective in detecting cyber attacks.https://dergipark.org.tr/tr/download/article-file/1817405web securitymachine learningcyber attackcyber securityartificial intelligence |
| spellingShingle | Mesut TOĞAÇAR Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications Gazi Üniversitesi Fen Bilimleri Dergisi web security machine learning cyber attack cyber security artificial intelligence |
| title | Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications |
| title_full | Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications |
| title_fullStr | Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications |
| title_full_unstemmed | Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications |
| title_short | Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications |
| title_sort | measuring the security effectiveness of machine learning methods used against cyber attacks in web applications |
| topic | web security machine learning cyber attack cyber security artificial intelligence |
| url | https://dergipark.org.tr/tr/download/article-file/1817405 |
| work_keys_str_mv | AT mesuttogacar measuringthesecurityeffectivenessofmachinelearningmethodsusedagainstcyberattacksinwebapplications |