Malware visualizer: A web apps malware family classification with machine learning - Mohd Zamri, Osman; Ahmad Firdaus, Zainal Abidin; Rahiwan Nazar, Romli (2021)
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.