MADLIRA

MADLIRA Malware detection using learning and information retrieval for Android Overview MADLIRA is a tool for Android malware detection. It consists in two components: TFIDF component and SVM learning component. In gerneral, it takes an input a set of malwares and benwares and then extracts the...

BIDO

BIDO This code belongs to "BIDO: A Unified Approach to Address Obfuscation and Concept Drift Challenges in Image-based Malware Detection". Author Author: Junhui Li1, Chengbin Feng2, Zhiwei Yang1, Qi Mo1 and Wei Wang1. Institution Institution1：Software School, Yunnan University, Kunming, Yunnan...

Empirical Evaluation of SMOTE in Android Malware Detection with Machine Learning: Challenges and Performance in CICMalDroid 2020

Malware, malicious software designed to damage computer systems and perpetrate scams, is proliferating at an alarming rate, with thousands of new threats emerging daily. Android devices, prevalent in smartphones, smartwatches, tablets, and IoTs, represent a vast attack surface, making malware...

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations

In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain...

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection

Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning ML algorithms have been applied to Android...