Related papers: Decoding Android Malware with a Fraction of Featur…
Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign…
Machine learning based solutions have been successfully employed for automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen…
Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however…
The rising use of Large Language Models (LLMs) to create and disseminate malware poses a significant cybersecurity challenge due to their ability to generate and distribute attacks with ease. A single prompt can initiate a wide array of…
Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these…
While graph-based Android malware classifiers achieve over 94% accuracy on standard benchmarks, they exhibit a significant generalization gap under distribution shift, suffering up to 45% performance degradation when encountering unseen…
An important problem of cyber-security is malware analysis. Besides good precision and recognition rate, a malware detection scheme needs to be able to generalize well for novel malware families (a.k.a zero-day attacks). It is important…
Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and…
Feature selection is vital for building effective predictive models, as it reduces dimensionality and emphasizes key features. However, current research often suffers from limited benchmarking and reliance on proprietary datasets. This…
In this paper, we propose a framework for early-stage malware detection and mitigation by leveraging natural language processing (NLP) techniques and machine learning algorithms. Our primary contribution is presenting an approach for…
With the popularity of Android growing exponentially, the amount of malware has significantly exploded. It is arguably one of the most viral problems on mobile platforms. Recently, various approaches have been introduced to detect Android…
It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have…
Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the…
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…
Nowadays, Android is the most dominant operating system in the mobile ecosystem, with billions of people using its apps daily. As expected, this trend did not go unnoticed by miscreants, and Android became the favorite platform for…
Static feature-based Android malware detection using machine learning (ML) remains critical due to its scalability and efficiency. However, existing approaches often overlook security-critical reproducibility concerns, such as dataset…
Machine learning is a key tool for Android malware detection, effectively identifying malicious patterns in apps. However, ML-based detectors are vulnerable to evasion attacks, where small, crafted changes bypass detection. Despite progress…
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…
The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious…
We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features. The primary inputs are the opcode sequence and the requested permissions of a given Android APK file. To reach a…