Pinned Repositories
A-Multimodal-Deep-Learning-Method-for-Android-Malware-Detection
Source code for Android malware analysis
AASC-Android-Application-Signature-Creation-Through-Graphs
active-learning
Continuous Learning for Android Malware Detection (USENIX Security 2023)
AMDs
A Comprehensive Study of Learning-based Android Malware Detectors under Challenging Environments
android-detectors
End-to-end implementation of ML-based Android malware detectors.
android-malware-classification
Android-Malware-Data-Augmentation-Using-WGAN
android-malware-detection
Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.
Android_Malware_Analysis
This project provides an analysis of Android malware using static features. We leverage Jupyter Notebook to perform static analysis on Android permissions and APK files, extracting relevant features and providing insights into potential malicious behavior.
Android_Malware_Detection_Method_Based_on_Graph_Attention_Networks
S0urc-3's Repositories
S0urc-3/AASC-Android-Application-Signature-Creation-Through-Graphs
S0urc-3/android-malware-classification
S0urc-3/android-malware-detection
Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.
S0urc-3/APISeqFewShot
FewShot Malware Classification based on API call sequences, also as code repo for "A Novel Few-Shot Malware Classification Approach for Unknown Family Recognition with Multi-Prototype Modeling" paper.
S0urc-3/axplorer
axplorer - Android Permission Mappings
S0urc-3/CallGraph-Flowdroid
Extract call graph from apks using Flowdroid.
S0urc-3/CNNXGB
S0urc-3/DeepLearning
S0urc-3/drebin
Drebin - NDSS 2014 Re-implementation
S0urc-3/Dynamo
Dynamic Analysis Tool of the Android Application Framework
S0urc-3/Git_workstream
hello-git
S0urc-3/GRAMAC-A-Graph-Based-Malware-Classification-Mechanism
S0urc-3/GraphDroid
A First Step Towards Explainable Static Detection of Android Malware with GNN.
S0urc-3/HP-MDC
HP-MDC is an Android Malware Detection (one of the classic pattern recognition issues) framework. It combines malware’s patterns from network traffic and code graph structure.
S0urc-3/learngit
S0urc-3/LibRadar
LibRadar - A detecting tool for 3rd-party libraries in Android apps.
S0urc-3/Looking-beyond-Dalvik-Bytecode
S0urc-3/lstm_malware_detection
S0urc-3/malnet-image
A large-scale database of malicious software images
S0urc-3/malwaresdetection
S0urc-3/MaMadroid
A new version used Androguard but not Soot to realize MaMadroid。
S0urc-3/MCBG
Source code of Malware Classification by Learning Semantic and Structural Features of Control Flow Graphs (TrustCom 2021)
S0urc-3/mlw_classification_hydra
S0urc-3/PetaDroid
S0urc-3/Reproduction-of-Android-Malware-detection-approaches
S0urc-3/ResMLP-pytorch
ResMLP: Feedforward networks for image classification with data-efficient training
S0urc-3/RGB-based-Andorid-Malware-detection
S0urc-3/Robust-Android-Malware-Detection-Against-Adversarial-Example-Attacks
S0urc-3/S3Feature
A Static Sensitive Subgraph-based Feature for Android Malware Detection
S0urc-3/simplify
Android virtual machine and deobfuscator