Related papers: Graph Neural Network-based Android Malware Classif…
This paper introduces a malware detection system for smartphones based on studying the dynamic behavior of suspicious applications. The main goal is to prevent the installation of the malicious software on the victim systems. The approach…
Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning(ML) techniques have been shown to be effective at detecting malware for Android, a comprehensive…
Graph-based detection methods leveraging Function Call Graphs (FCGs) have shown promise for Android malware detection (AMD) due to their semantic insights. However, the deployment of malware detectors in dynamic and hostile environments…
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 However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign…
As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective. In this work, we propose a…
The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services,…
As malware continues to become increasingly sophisticated, threatening, and evasive, malware detection systems must keep pace and become equally intelligent, powerful, and transparent. In this paper, we propose Assembly Flow Graph (AFG) to…
In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid…
With the escalating threat of malware, particularly on mobile devices, the demand for effective analysis methods has never been higher. While existing security solutions, including AI-based approaches, offer promise, their lack of…
It is well-known that Android malware constantly evolves so as to evade detection. This causes the entire malware population to be non-stationary. Contrary to this fact, most of the prior works on Machine Learning based Android malware…
Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with…
The Android operating system is the most spread mobile platform in the world. Therefor attackers are producing an incredible number of malware applications for Android. Our aim is to detect Android's malware in order to protect the user. To…
With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patterns from Android apps. However, the lack of an in-depth and…
Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applications, there is still…
The growth in the number of Android and Internet of Things (IoT) devices has witnessed a parallel increase in the number of malicious software (malware), calling for new analysis approaches. We represent binaries using their graph…
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as…
With the proliferation of Android malware, the demand for an effective and efficient malware detection system is on the rise. The existing device-end learning based solutions tend to extract limited syntax features (e.g., permissions and…
Android is the predominant mobile operating system for the past few years. The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design…
Smartphones have become an intrinsic part of human's life. The smartphone unifies diverse advanced characteristics. It enables users to store various data such as photos, health data, credential bank data, and personal information. The…
This study investigates fraud detection in ride hailing platforms through Graph Neural Networks (GNNs),focusing on the effectiveness of various models. By analyzing prevalent fraudulent activities, the research highlights and compares the…