Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine
<p class="BodyAbstract"><span lang="IN">Android Malware has grown significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Darussalam Gontor
2018-05-01
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| Series: | Fountain of Informatics Journal |
| Subjects: | |
| Online Access: | https://ejournal.unida.gontor.ac.id/index.php/FIJ/article/view/1875 |
| _version_ | 1797944623114485760 |
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| author | Hendra Saputra Setio Basuki Mahar Faiqurahman |
| author_facet | Hendra Saputra Setio Basuki Mahar Faiqurahman |
| author_sort | Hendra Saputra |
| collection | DOAJ |
| description | <p class="BodyAbstract"><span lang="IN">Android Malware has grown significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each type of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type will be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver. To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used is Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). A result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicates that permission and broadcast receiver can be used in classifying type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection.</span></p> |
| first_indexed | 2024-04-10T20:41:28Z |
| format | Article |
| id | doaj.art-63100c13405d4987aa226ef9e815304c |
| institution | Directory Open Access Journal |
| issn | 2541-4313 2548-5113 |
| language | English |
| last_indexed | 2024-04-10T20:41:28Z |
| publishDate | 2018-05-01 |
| publisher | University of Darussalam Gontor |
| record_format | Article |
| series | Fountain of Informatics Journal |
| spelling | doaj.art-63100c13405d4987aa226ef9e815304c2023-01-24T18:21:16ZengUniversity of Darussalam GontorFountain of Informatics Journal2541-43132548-51132018-05-0131121810.21111/fij.v3i1.18751102Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector MachineHendra Saputra0Setio Basuki1Mahar Faiqurahman2Universitas Muhammadiyah MalangUniversitas Muhammadiyah MalangUniversitas Muhammadiyah Malang<p class="BodyAbstract"><span lang="IN">Android Malware has grown significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each type of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type will be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver. To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used is Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). A result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicates that permission and broadcast receiver can be used in classifying type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection.</span></p>https://ejournal.unida.gontor.ac.id/index.php/FIJ/article/view/1875classification android malware, feature selection, svm and multi class svm one against one |
| spellingShingle | Hendra Saputra Setio Basuki Mahar Faiqurahman Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine Fountain of Informatics Journal classification android malware, feature selection, svm and multi class svm one against one |
| title | Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine |
| title_full | Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine |
| title_fullStr | Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine |
| title_full_unstemmed | Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine |
| title_short | Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine |
| title_sort | implementasi teknik seleksi fitur pada klasifikasi malware android menggunakan support vector machine |
| topic | classification android malware, feature selection, svm and multi class svm one against one |
| url | https://ejournal.unida.gontor.ac.id/index.php/FIJ/article/view/1875 |
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