Malware visualizer: A web apps malware family classification with machine learning
Within the past few years, malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. Many solutions in the past have proven to be ineffective and result...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34538/ http://umpir.ump.edu.my/id/eprint/34538/1/Malware%20visualizer_A%20web%20apps%20malware%20family%20classification%20with%20machine%20learning.CITREX2021..pdf |
| _version_ | 1848824533834792960 |
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| author | Mohd Zamri, Osman Ahmad Firdaus, Zainal Abidin Rahiwan Nazar, Romli |
| author_facet | Mohd Zamri, Osman Ahmad Firdaus, Zainal Abidin Rahiwan Nazar, Romli |
| author_sort | Mohd Zamri, Osman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Within the past few years, malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. Many solutions in the past have proven to be ineffective and result many false positives. Other than that, most of the solution focuses on the android apk file, instead of visualizing the apk into image-based form. The objective of this project is to build a web apps to classify malware by transforming the apk file into image-based representation. This project uses three classification algorithm which are Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The web apps is developed using Python with help of Streamlit with is a Python library for building datadriven web apps. The dataset contains 25 malware classes ranging from Trojan Horses to Spyware and 1 legitimate application class. |
| first_indexed | 2025-11-15T03:14:33Z |
| format | Conference or Workshop Item |
| id | ump-34538 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:14:33Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-345382022-06-28T06:27:22Z http://umpir.ump.edu.my/id/eprint/34538/ Malware visualizer: A web apps malware family classification with machine learning Mohd Zamri, Osman Ahmad Firdaus, Zainal Abidin Rahiwan Nazar, Romli QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Within the past few years, malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. Many solutions in the past have proven to be ineffective and result many false positives. Other than that, most of the solution focuses on the android apk file, instead of visualizing the apk into image-based form. The objective of this project is to build a web apps to classify malware by transforming the apk file into image-based representation. This project uses three classification algorithm which are Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The web apps is developed using Python with help of Streamlit with is a Python library for building datadriven web apps. The dataset contains 25 malware classes ranging from Trojan Horses to Spyware and 1 legitimate application class. 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34538/1/Malware%20visualizer_A%20web%20apps%20malware%20family%20classification%20with%20machine%20learning.CITREX2021..pdf Mohd Zamri, Osman and Ahmad Firdaus, Zainal Abidin and Rahiwan Nazar, Romli (2021) Malware visualizer: A web apps malware family classification with machine learning. In: Creation, Innovation, Technology & Research Exposition (CITREX) 2021 , 2021 , Virtually hosted by Universiti Malaysia Pahang. p. 1.. (Published) |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Mohd Zamri, Osman Ahmad Firdaus, Zainal Abidin Rahiwan Nazar, Romli Malware visualizer: A web apps malware family classification with machine learning |
| title | Malware visualizer: A web apps malware family classification with machine learning |
| title_full | Malware visualizer: A web apps malware family classification with machine learning |
| title_fullStr | Malware visualizer: A web apps malware family classification with machine learning |
| title_full_unstemmed | Malware visualizer: A web apps malware family classification with machine learning |
| title_short | Malware visualizer: A web apps malware family classification with machine learning |
| title_sort | malware visualizer: a web apps malware family classification with machine learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/34538/ http://umpir.ump.edu.my/id/eprint/34538/1/Malware%20visualizer_A%20web%20apps%20malware%20family%20classification%20with%20machine%20learning.CITREX2021..pdf |