MALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION
<p align="justify">Microsoft Windows is popular operating system and malware technologies are also evolving in this operating system. When malware access the Windows API, it will leave a trail of activity sequences. From the sequence of this activity, researchers can differentiate ma...
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id-itb.:249842018-03-16T14:30:48ZMALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION SENO AJI (NIM : 23214019), ADHITYO Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/24984 <p align="justify">Microsoft Windows is popular operating system and malware technologies are also evolving in this operating system. When malware access the Windows API, it will leave a trail of activity sequences. From the sequence of this activity, researchers can differentiate malware and benign. Research has been done by converting the activity sequences into the Windows API category. This research used 48 API <br /> <br /> category. Then the malware and benign are classified using machine learning with stacked generalization algorithm. Research used 1052 samples (526 malware and 526 benign) and split it to 50% for training and 50% for testing. The result showed that it can detect malware with highest accuracy 98.1%.<p align="justify"> text |
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Institut Teknologi Bandung |
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Indonesia Indonesia |
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<p align="justify">Microsoft Windows is popular operating system and malware technologies are also evolving in this operating system. When malware access the Windows API, it will leave a trail of activity sequences. From the sequence of this activity, researchers can differentiate malware and benign. Research has been done by converting the activity sequences into the Windows API category. This research used 48 API <br />
<br />
category. Then the malware and benign are classified using machine learning with stacked generalization algorithm. Research used 1052 samples (526 malware and 526 benign) and split it to 50% for training and 50% for testing. The result showed that it can detect malware with highest accuracy 98.1%.<p align="justify"> |
| format |
Theses |
| author |
SENO AJI (NIM : 23214019), ADHITYO |
| spellingShingle |
SENO AJI (NIM : 23214019), ADHITYO MALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION |
| author_facet |
SENO AJI (NIM : 23214019), ADHITYO |
| author_sort |
SENO AJI (NIM : 23214019), ADHITYO |
| title |
MALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION |
| title_short |
MALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION |
| title_full |
MALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION |
| title_fullStr |
MALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION |
| title_full_unstemmed |
MALWARE DETECTION ON WINDOWS 10 OPERATION SYSTEM USING DINAMIC ANALYSIS AND STACKED GENERALIZATION |
| title_sort |
malware detection on windows 10 operation system using dinamic analysis and stacked generalization |
| url |
https://digilib.itb.ac.id/gdl/view/24984 |
| _version_ |
1822020556761858048 |