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...

Android Malware Spotted Subscribing Victims to Paid Services Without Consent

Cybersecurity researchers expose a 10-month global Android malware campaign using fake apps to secretly charge users through premium SMS bills...

FEMITBOT Network Abuses Telegram Mini Apps for Crypto Scams and Android Malware

A massive fraud network called FEMITBOT uses Telegram Mini Apps and fake brand names like Apple, Disney, and…...

MARD: A Multi-Agent Framework for Robust Android Malware Detection

With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow features, lacking deep semantic understanding and interpretability of decisions. Although Large Language Models LLMs...

Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset

Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actual release times, inflating accuracy and weakening real-world robustness. We address this by constructing a time-stamped dataset of benign and...

Label-Efficient Training Updates for Malware Detection over Time

Machine Learning ML-based detectors are becoming essential to counter the proliferation of malware. However, common ML algorithms are not designed to cope with the dynamic nature of real-world settings, where both legitimate and malicious software evolve. This distribution drift causes models...

$300 a Month Android Malware ‘Oblivion’ Uses Fake Updates to Hijack Phones

Cybersecurity researchers at Certo reveal Oblivion, a new Android Trojan targeting major brands like Samsung and Xiaomi. It bypasses security to steal passwords and bank codes...

New ZeroDayRAT Malware Claims Full Monitoring of Android and iOS Devices

Meet ZeroDayRAT, a newly advertised malware targeting Android and iOS devices with surveillance, location tracking, and crypto theft tools sold via Telegram as a MaaS service...

AndroWasm: An Empirical Study on Android Malware Obfuscation through WebAssembly

In recent years, stealthy Android malware has increasingly adopted sophisticated techniques to bypass automatic detection mechanisms and harden manual analysis. Adversaries typically rely on obfuscation, anti-repacking, steganography, poisoning, and evasion techniques to AI-based tools, and...

PromptSpy Android Malware Abuses Gemini AI to Automate Recent-Apps Persistence

Cybersecurity researchers have discovered what they say is the first Android malware that abuses Gemini, Google's generative artificial intelligence AI chatbot, as part of its execution flow and achieves persistence. The malware has been codenamed PromptSpy by ESET. The malware is equipped to...

Fake IPTV Apps Spread Massiv Android Malware Targeting Mobile Banking Users

Cybersecurity researchers have disclosed details of a new Android trojan called Massiv that's designed to facilitate device takeover DTO attacks for financial theft. The malware, according to ThreatFabric, masquerades as seemingly harmless IPTV apps to deceive victims, indicating that the activit...

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...

Arsink Spyware Posing as WhatsApp, YouTube, Instagram, TikTok Hits 143 Countries

Another day, another Android malware campaign targeting unsuspecting users worldwide by masquerading as popular apps...

Malware Classification Using Diluted Convolutional Neural Network with Fast Gradient Sign Method

Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and sophistication, the mitigation and detection of these malicious...

Integrating APK Image and Text Data for Enhanced Threat Detection: A Multimodal Deep Learning Approach to Android Malware

As zero-day Android malware attacks grow more sophisticated, recent research highlights the effectiveness of using image-based representations of malware bytecode to detect previously unseen threats. However, existing studies often overlook how image type and resolution affect detection and ignor...

LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors

The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning ML techniques for scalable and accurate malware detection. Despite their effectiveness, these models remain vulnerable to adversarial attacks that introduce carefully crafte...

Frogblight Malware Targets Android Users With Fake Court and Aid Apps

Kaspersky warns of 'Frogblight,' a new Android malware draining bank accounts in Turkiye. Learn how this 'court case' scam steals your data and how to stay safe...

IoT-Based Android Malware Detection Using Graph Neural Network with Adversarial Defense

Since the Internet of Things IoT is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings...

Kimsuky Spreads DocSwap Android Malware via QR Phishing Posing as Delivery App

The North Korean threat actor known as Kimsuky has been linked to a new campaign that distributes a new variant of Android malware called DocSwap via QR codes hosted on phishing sites mimicking Seoul-based logistics firm CJ Logistics formerly CJ Korea Express. "The threat actor leveraged QR codes...