Malware visualizer: A web apps malware family classification with machine learning

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..

Malware visualizer_A web apps malware family classification with machine learning.CITREX2021..pdf

Download (438kB) | Preview


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.

Item Type: Conference or Workshop Item (Poster)
Uncontrolled Keywords: CITREX 2021, poster
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 28 Jun 2022 06:27
Last Modified: 28 Jun 2022 06:27
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item