amtracker

amtracker Rastreador de malware Android Originalmente esto era parte de un módulo para el framework que estoy desarrollando constantemente. Los decodificadores utilizaban principalmente Androguard para extraer el C2 o las credenciales de los autores del malware. Uno de los decodificadores utiliza...

MalwareSourceCode

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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...

DroidDetective

🕵️ Un framework de análisis de malware para aplicaciones Android basado en machine learning. ☢️ DroidDetective es una herramienta Python para analizar aplicaciones Android APKs en busca de comportamientos y configuraciones potencialmente maliciosas. Al proporcionar una ruta a una aplicación archivo...

LiveHiddenCamera

Telecamera Nascosta in Diretta Telecamera Nascosta in Diretta LHC è una libreria che registra video e audio live da un dispositivo Android senza mostrare un'anteprima. Motivazione Sto lavorando a un malware Android basato su ricerche e ho deciso di creare un RAT Strumento di Amministrazione Remot...

engine

Droidefense Engine Erweitertes Android-Malware-Analyse-Framework Neueste Version Herunterladen Was Droidefense ist Droidefense ursprünglich atom genannt: a nalysis t hrough o bservation m achine ist der Codename für ein Werkzeug zur Analyse/Reverse-Engineering von Android-Apps/Malware. Es wurde m...

cuckoo-droid

CuckooDroid - Analisi automatizzata di malware Android. Contribuito da Check Point Software Technologies LTD. CuckooDroid è un'estensione di Cuckoo Sandbox, il software Open Source per automatizzare l'analisi di file sospetti. CuckooDroid porta a Cuckoo le capacità di esecuzione e analisi delle...

silverboxcc

Malware Android sottoposto a reverse engineering chiamato Red Alert V2.0. Al momento dell'analisi, non erano più in esecuzione server C2, quindi non siamo stati in grado di osservare alcun traffico tra il malware e il server C2. Così abbiamo ricostruito il protocollo C2 e scritto questo pannello ...

MalConfig

MalConfig Dies ist Teil eines Moduls für das Framework, das ich ständig weiterentwickle. Roboter-Bilder werden von robohash.org bereitgestellt. Flaggen-Symbole werden von famfamfam bereitgestellt. Die Decoder nutzten hauptsächlich Androguard, um die C2- oder Malware-Autoren-Zugangsdaten zu...

MalEval

MalEval Articolo: Basta “sapere che è dannoso”? Valutare gli LLM per l'audit granulare del comportamento del malware DOI dell'articolo: 10.1145/3832187 MalEval è un framework per valutare i report sul comportamento del malware Android generati da grandi modelli linguistici. Il codice in questo...

maldrolyzer

maldrolyzer Estrutura simples para extrair dados "acionáveis" de malware Android C&Cs, números de telefone etc. Instalação Você precisa instalar os seguintes pacotes antes de começar a usar este projeto: Androguard git clone https://github.com/androguard/androguard; cd androguard; sudo python...

BIDO

BIDO Questo codice appartiene a "BIDO: Un approccio unificato per affrontare l'offuscamento degli indirizzi e le sfide della deriva concettuale nel rilevamento di malware basato su immagini". Autore Autore: Junhui Li1, Chengbin Feng2, Zhiwei Yang1, Qi Mo1 e Wei Wang1. Istituzione...

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 è uno strumento per divulgare il modello di rilevamento dei sistemi di rilevamento malware Android cioè, i software antivirus e bypassare le loro logiche di rilevamento utilizzando le informazioni divulgate insieme a tecniche di offuscamento APK. AVPASS non si limita alle...

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...