An empirical evaluation of the interpretation methods on Malware analysis
Malware (malicious software) is a type of software design to damage or abuse any programmable system or network. Most malware do not draw attention to themselves and cannot be seen with the naked eye. Therefore, malware analysis is needed as it is the process of getting to know the behavior and moti...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157254 |
| _version_ | 1826117750909566976 |
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| author | Lee, Andrew Jian Hao |
| author2 | Liu Yang |
| author_facet | Liu Yang Lee, Andrew Jian Hao |
| author_sort | Lee, Andrew Jian Hao |
| collection | NTU |
| description | Malware (malicious software) is a type of software design to damage or abuse any programmable system or network. Most malware do not draw attention to themselves and cannot be seen with the naked eye. Therefore, malware analysis is needed as it is the process of getting to know the behavior and motive of suspicious files or Uniform Resource Locator (URL). Malware analysis can be conducted in 2 manners, static, dynamic, or even both. Static analysis is the testing and evaluation of the internal structure of the application while running it. Dynamic analysis does the total opposite of static analysis where it tests and evaluate on the application during runtime.
Throughout the period of my FYP, we will be building up a machine learning model. We will be applying interpretation method of Tensorflow as our source platform for machine learning. To generate our model, we use Keras as training for deep learning models. To evaluate the accuracy of the model, we will be using functional model which allows to build random graphs of layers. |
| first_indexed | 2024-10-01T04:32:28Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157254 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:32:28Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1572542022-05-11T13:18:21Z An empirical evaluation of the interpretation methods on Malware analysis Lee, Andrew Jian Hao Liu Yang School of Computer Science and Engineering yangliu@ntu.edu.sg Engineering::Computer science and engineering Malware (malicious software) is a type of software design to damage or abuse any programmable system or network. Most malware do not draw attention to themselves and cannot be seen with the naked eye. Therefore, malware analysis is needed as it is the process of getting to know the behavior and motive of suspicious files or Uniform Resource Locator (URL). Malware analysis can be conducted in 2 manners, static, dynamic, or even both. Static analysis is the testing and evaluation of the internal structure of the application while running it. Dynamic analysis does the total opposite of static analysis where it tests and evaluate on the application during runtime. Throughout the period of my FYP, we will be building up a machine learning model. We will be applying interpretation method of Tensorflow as our source platform for machine learning. To generate our model, we use Keras as training for deep learning models. To evaluate the accuracy of the model, we will be using functional model which allows to build random graphs of layers. Bachelor of Engineering (Computer Engineering) 2022-05-11T13:18:21Z 2022-05-11T13:18:21Z 2022 Final Year Project (FYP) Lee, A. J. H. (2022). An empirical evaluation of the interpretation methods on Malware analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157254 https://hdl.handle.net/10356/157254 en SCSE21-0220 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Lee, Andrew Jian Hao An empirical evaluation of the interpretation methods on Malware analysis |
| title | An empirical evaluation of the interpretation methods on Malware analysis |
| title_full | An empirical evaluation of the interpretation methods on Malware analysis |
| title_fullStr | An empirical evaluation of the interpretation methods on Malware analysis |
| title_full_unstemmed | An empirical evaluation of the interpretation methods on Malware analysis |
| title_short | An empirical evaluation of the interpretation methods on Malware analysis |
| title_sort | empirical evaluation of the interpretation methods on malware analysis |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/157254 |
| work_keys_str_mv | AT leeandrewjianhao anempiricalevaluationoftheinterpretationmethodsonmalwareanalysis AT leeandrewjianhao empiricalevaluationoftheinterpretationmethodsonmalwareanalysis |