Related papers: DroidDissector: A Static and Dynamic Analysis Tool…
The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online…
Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. Machine learning, leading to a great evolution in Android malware detection in recent years, is typically applied in the…
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…
We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and…
The pervasiveness of the Android operating system, with the availability of applications almost for everything, is readily accessible in the official Google play store or a dozen alternative third-party markets. Additionally, the vital role…
Our computer systems for decades have been threatened by various types of hardware and software attacks of which Malwares have been one of them. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even…
The widespread use of Android applications has made them a prime target for cyberattacks, significantly increasing the risk of malware that threatens user privacy, security, and device functionality. Effective malware detection is thus…
The number of Android malware variants (clones) are on the rise and, to stop this attack of clones we need to develop new methods and techniques for analysing and detecting them. As a first step, we need to study how these malware clones…
With the increasing prevalence of fraudulent Android applications such as fake and malicious applications, it is crucial to detect them with high accuracy and adaptability. We present AgentDroid, a novel tool for Android fraudulent…
Android has become the most popular mobile operating system. Correspondingly, an increasing number of Android malware has been developed and spread to steal users' private information. There exists one type of malware whose benign behaviors…
Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user.…
The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To address this, we…
In the past decade, the cyber-crime related to mobile devices has increased. Mobile devices, especially the ones running on Android operating system are particularly interesting to malware creators, as the users often keep the biggest…
In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features. In particular, we focus our attention on the permissions requested by an application. We consider both binary…
This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android…
As Android has become increasingly popular, so has malware targeting it, thus pushing the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes…
With the widespread adoption of smartphones, Android malware has become a significant challenge in the field of mobile device security. Current Android malware detection methods often rely on feature engineering to construct dynamic or…
Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen in the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow, stressing the…
Today's mobile platforms provide only coarse-grained permissions to users with regard to how third- party applications use sensitive private data. Unfortunately, it is easy to disguise malware within the boundaries of legitimately-granted…
We present DroidGen a tool for automatic anti-malware policy inference. DroidGen employs a data-driven approach: it uses a training set of malware and benign applications and makes call to a constraint solver to generate a policy under…