Related papers: Detecting Android Malware by Visualizing App Behav…

In the development and maintenance of Android apps, the quick and accurate reproduction of user-reported bugs is crucial to ensure application quality and improve user satisfaction. However, this process is often time-consuming and complex.…

Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to…

Android malware presents a persistent threat to users' privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detection (AMD) systems. However, adversarial Android malware…

Repackaging is a technique that has been increasingly adopted by authors of Android malware. The main problem facing the research community working on devising techniques to detect this breed of malware is the lack of ground truth that…

In this paper a novel system for detecting meaningful deviations in a mobile application's network behavior is proposed. The main goal of the proposed system is to protect mobile device users and cellular infrastructure companies from…

Due to the continuous improvement of performance and functions, Android remains the most popular operating system on mobile phone today. However, various malicious applications bring great threats to the system. Over the past few years,…

With the rapid advancement of large language models (LLMs), mobile agents have emerged as promising tools for phone automation, simulating human interactions on screens to accomplish complex tasks. However, these agents often suffer from…

Recent advancements in artificial intelligence (AI) and its widespread integration into mobile software applications have received significant attention, highlighting the growing prominence of AI capabilities in modern software systems.…

Machine learning (ML)-based Android malware detection has been one of the most popular research topics in the mobile security community. An increasing number of research studies have demonstrated that machine learning is an effective and…

Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem,…

The malware analysis and detection research community relies on the online platform VirusTotal to label Android apps based on the scan results of around 60 antiviral scanners. Unfortunately, there are no standards on how to best interpret…

According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively ~99% on the Android platform. Malware writers are applying stealthy mutations (obfuscations) to…

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware…

Machine learning (ML) has demonstrated significant advancements in Android malware detection (AMD); however, the resilience of ML against realistic evasion attacks remains a major obstacle for AMD. One of the primary factors contributing to…

Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of…

Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with active learning to…

Machine learning-based Android malware classifiers achieve high accuracy in stationary environments but struggle with concept drift. The rapid evolution of malware, especially with new families, can depress classification accuracy to…

Generative Adversarial Networks (GANs) have demonstrated their versatility across various applications, including data augmentation and malware detection. This research explores the effectiveness of utilizing GAN-generated data to train a…

The behavior of malware threats is gradually increasing, heightened the need for malware detection. However, existing malware detection methods only target at the existing malicious samples, the detection of fresh malicious code and…

According to the World Health Organization(WHO), it is estimated that approximately 1.3 billion people live with some forms of vision impairment globally, of whom 36 million are blind. Due to their disability, engaging these minority into…