Intensive Malware Detection Approach based on Data Mining
Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via online connections and mobile devices. W...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2023-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
| Subjects: | |
| Online Access: | https://www.yrpipku.com/journal/index.php/jaets/article/view/2865 |
| _version_ | 1797208944917086208 |
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| author | Israa Ezzat Salem Karim Hashim Al-Saedi |
| author_facet | Israa Ezzat Salem Karim Hashim Al-Saedi |
| author_sort | Israa Ezzat Salem |
| collection | DOAJ |
| description |
Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via online connections and mobile devices. While malware has always been a problem in the digital age, its effects have gotten increasingly serious. Traditional malware detection methods seek to locate specific malware samples and families to recognize harmful codes and can be located using traditional signature- and rule-based detection methods. The research focuses on developing malware detectors using data mining techniques. The proposed method outlined below sets itself apart by emphasizing the processing of malware behaviors significantly dependent on aspects. Finding more dependable intelligent detecting techniques is a crucial component of this paper. In order to identify the cluster of the most essential malware features and use decision tree classifiers for malware detection, the study, a common methodology for creating malware detectors based on data mining, is implemented and investigated. Our approach can identify the most significant features of malware that can significantly determine and detect a malware code.
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| first_indexed | 2024-03-07T14:16:45Z |
| format | Article |
| id | doaj.art-ae6743584a124f3b80b710ae42f7b351 |
| institution | Directory Open Access Journal |
| issn | 2715-6087 2715-6079 |
| language | English |
| last_indexed | 2024-04-24T09:46:51Z |
| publishDate | 2023-12-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj.art-ae6743584a124f3b80b710ae42f7b3512024-04-14T12:07:59ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792023-12-015110.37385/jaets.v5i1.2865Intensive Malware Detection Approach based on Data MiningIsraa Ezzat Salem0Karim Hashim Al-Saedi1Computer Science Department, College of Science, Mustansiriyah University, Baghdad, IraqComputer Science Department, College of Science, Mustansiriyah University, Baghdad, Iraq Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via online connections and mobile devices. While malware has always been a problem in the digital age, its effects have gotten increasingly serious. Traditional malware detection methods seek to locate specific malware samples and families to recognize harmful codes and can be located using traditional signature- and rule-based detection methods. The research focuses on developing malware detectors using data mining techniques. The proposed method outlined below sets itself apart by emphasizing the processing of malware behaviors significantly dependent on aspects. Finding more dependable intelligent detecting techniques is a crucial component of this paper. In order to identify the cluster of the most essential malware features and use decision tree classifiers for malware detection, the study, a common methodology for creating malware detectors based on data mining, is implemented and investigated. Our approach can identify the most significant features of malware that can significantly determine and detect a malware code. https://www.yrpipku.com/journal/index.php/jaets/article/view/2865Malware detectionDecision treeMachine learningIdentify malware attack |
| spellingShingle | Israa Ezzat Salem Karim Hashim Al-Saedi Intensive Malware Detection Approach based on Data Mining Journal of Applied Engineering and Technological Science Malware detection Decision tree Machine learning Identify malware attack |
| title | Intensive Malware Detection Approach based on Data Mining |
| title_full | Intensive Malware Detection Approach based on Data Mining |
| title_fullStr | Intensive Malware Detection Approach based on Data Mining |
| title_full_unstemmed | Intensive Malware Detection Approach based on Data Mining |
| title_short | Intensive Malware Detection Approach based on Data Mining |
| title_sort | intensive malware detection approach based on data mining |
| topic | Malware detection Decision tree Machine learning Identify malware attack |
| url | https://www.yrpipku.com/journal/index.php/jaets/article/view/2865 |
| work_keys_str_mv | AT israaezzatsalem intensivemalwaredetectionapproachbasedondatamining AT karimhashimalsaedi intensivemalwaredetectionapproachbasedondatamining |