MalwareSourceCode

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MalEval

MalEval Article: Is “Knowing It’s Malicious” Enough? Evaluating LLMs for Fine-Grained Malware Behavior Auditing Article DOI: 10.1145/3832187 MalEval is a framework for evaluating Android malware behavior reports generated by large language models. The code in this repository implements two...

DroidDetective

🕵️ A machine learning malware analysis framework for Android apps. ☢️ DroidDetective is a Python tool for analysing Android applications APKs for potential malware related behaviour and configurations. When provided with a path to an application APK file Droid Detective will make a prediction using...

MalConfig

MalConfig This is part of a module for the framework that i'm constantly developing. Robots images are provided by robohash.org Flag icons are provided by famfamfam The decoders were mostly making use of Androguard to extract the C2 or malware authors' credentials One of the decoder is making use...

amtracker

amtracker Android Malware Tracker This was originally part of a module for the framework that i'm constantly developing. The decoders were mostly making use of Androguard to extract the C2 or malware authors' credentials One of the decoder is making use of LIEF to extract the C2 from the android...

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...

LiveHiddenCamera

Live Hidden Camera Live Hidden Camera LHC is a library which record live video and audio from Android device without displaying a preview. Motivation I'm working on a research based Android Malware and decided to create a multi function RAT Remote Administration Tool. I was working on Live Stream...

avpass

AVPASS AVPASS is a tool for leaking the detection model of Android malware detection systems i.e., antivirus software, and bypassing their detection logics by using the leaked information coupled with APK obfuscation techniques. AVPASS is not limited to detection features used by detection system...

avpass

AVPASS AVPASSは、Androidマルウェア検出システム（すなわち、アンチウイルスソフトウェア）の検出モデルを漏洩し、漏洩した情報とAPK難読化技術を組み合わせてその検出ロジックを回避するためのツールです。AVPASSは検出システムが使用する検出特徴に限定されず、検出ルールも推論できるため、APKバイナリを自動的に変換することで、あらゆるAndroidマルウェアを良性アプリケーションに偽装できます。変換中のアプリケーションロジックの漏洩を防ぐために、AVPASSはImitation...

maldrolyzer

maldrolyzer Simple framework to extract "actionable" data from Android malware C&Cs, phone numbers etc. Installation You have to install the following packets before you start using this project: Androguard git clone https://github.com/androguard/androguard; cd androguard; sudo python setup.py...

silverboxcc

Reverse engineered android malware called Red Alert V2.0. At the time of analysis, there were no longer any C2 servers running and so we were unable to observe any traffic between the malware and the C2 server. So we figured out the C2 protocol and wrote this control panel. Bot's traffic from the...

cuckoo-droid

CuckooDroid - 自動化されたAndroidマルウェア解析。 Check Point Software Technologies LTD. より提供。 CuckooDroidは、疑わしいファイルの解析を自動化するオープンソースソフトウェアCuckoo Sandboxの拡張であり、CuckooにAndroidアプリケーションの実行と解析機能をもたらします。 インストール - 簡単な統合スクリプト: root@kitploit: git config --global user.email "[email protected]" git config --global...

engine

Droidefense Engine 高度なAndroidマルウェア解析フレームワーク 最新リリース ダウンロード Droidefenseとは Droidefense （元々のコードネームはatom: a nalysis t hrough o bservation m achine）は、Androidアプリ/マルウェアの解析・リバースエンジニアリングツールのコードネームです。セキュリティ上の問題や、マルウェア研究者が日常的に直面するトリックに焦点を当てて構築されました。マルウェアにアンチ解析...

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...

Manic Android Malware Exfiltrates Data From Offline Phones via Nearby Infected Devices

A new Android threat codenamed Manic has been observed actively targeting Ukrainian banks, government and identity services, and messaging applications, as well as Russian and European financial institutions, global fintech and cryptocurrency services, and military-focused communications. "Manic...

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although applications are required to undergo malware screening before being published on officia...

DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors

Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that, despite claims, existing problem-space attacks remain largely impractical. Most techniques leverage software transplantation to inject entire beni...

The Sound of Malware: A Memory Forensics Approach for Android Malware Analysis Via Audio Signals

Android malware analysis is currently facing increasing challenges in achieving robust classification and detecting stealth attacks. Modern threats employ advanced evasion strategies such as code obfuscation, dynamic loading, packing, and even steganographic manipulation of traditional static and...

Don't Trust Us: A Privacy-By-Design Android Malware Detection Pipeline

Android malware detection increasingly relies on collecting and processing sensitive user data, including device identifiers, network artifacts, and runtime traces, while privacy is too often treated as a secondary concern. Existing privacy-aware approaches typically enforce privacy after data...

Adversarial Vulnerability under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The dataset is organized into yearly slices and evaluated under three...