308 matches found
MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research
We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide range of features and extensive metadata. To ensure accurate malware classification, we employ the VirusTotal API,...
An In-Depth Analysis of Cyber Attacks in Secured Platforms
There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and...
APThreatHunter: An Automated Planning-Based Threat Hunting Framework
Cyber attacks threaten economic interests, critical infrastructure, and public health and safety. To counter this, entities adopt cyber threat hunting, a proactive approach that involves formulating hypotheses and searching for attack patterns within organisational networks. Automating cyber thre...
New HyperRat Android Malware Sold as Ready-Made Spy Tool
Researchers have uncovered HyperRat, a new Android malware sold as a service, giving attackers remote control, data theft tools, and mass phishing features...
Baohuo Android Malware Hijacks Telegram Accounts via Fake Telegram X
New Android malware Baohuo hijacks Telegram X accounts, stealing data and controlling chats. Over 58,000 devices infected, mainly in India and Brazil...
ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research
The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial...
DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a machine learning model into making incorrect predictions...
Fake VPN and streaming app drops malware that drains your bank account
Security researchers are warning Android users to delete a fake VPN and streaming app that can let criminals take over their phones and drain their bank accounts. The app, Mobdro Pro IP TV + VPN, was discovered by researchers at Cleafy to be a malicious sideloaded app, not a legitimate VPN. Their...
EUVD-2018-14343
Malware in sbrugna...
A week in security (September 29 – October 5)
Last week on Malwarebytes Labs: From threats to apology, hackers pull child data offline after public backlash Your Meta AI conversations may come back as ads in your feed Scam Facebook groups send malicious Android malware to seniors Sendit tricked kids, harvested their data, and faked messages,...
EUVD-2025-6755
Malicious code in bioql PyPI...
Scam Facebook groups send malicious Android malware to seniors
An infostealer and banking Trojan rolled into one is making the rounds in Facebook groups aimed at "active seniors". Attackers used social engineering methods to lure targets into joining fake Facebook groups that appeared to promote travel and community activities—such as trips, dance classes, a...
LLM-Generated Samples for Android Malware Detection
Android malware continues to evolve through obfuscation and polymorphism, posing challenges for both signature-based defenses and machine learning models trained on limited and imbalanced datasets. Synthetic data has been proposed as a remedy for scarcity, yet the role of large language models LL...
MalEval Android Malware Evaluation Framework
This repository contains the source code of MalEval, an evaluation framework for Android malware behavior auditing, focusing on explaining and substantiating malicious behaviors. The framework provides expert-verified reports, curated metadata, and model outputs to enable reproducible evaluation ...
Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing
Automated malware classification has achieved strong detection performance. Yet, malware behavior auditing seeks causal and verifiable explanations of malicious activities -- essential not only to reveal what malware does but also to substantiate such claims with evidence. This task is challengin...
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...
Feature-Centric Approaches to Android Malware Analysis: a Survey
Sophisticated malware families exploit the openness of the Android platform to infiltrate IoT networks, enabling large-scale disruption, data exfiltration, and denial-of-service attacks. This systematic literature review SLR examines cutting-edge approaches to Android malware analysis with direct...
RatOn Android Malware Detected With NFC Relay and ATS Banking Fraud Capabilities
A new Android malware called RatOn has evolved from a basic tool capable of conducting Near Field Communication NFC relay attacks to a sophisticated remote access trojan with Automated Transfer System ATS capabilities to conduct device fraud. "RatOn merges traditional overlay attacks with automat...
RatOn Android Malware Detected With NFC Relay and ATS Banking Fraud Capabilities
A new Android malware called RatOn has evolved from a basic tool capable of conducting Near Field Communication NFC relay attacks to a sophisticated remote access trojan with Automated Transfer System ATS capabilities to conduct device fraud. "RatOn merges traditional overlay attacks with automat...