Related papers: Explaining Black-box Android Malware Detection

Due to its open-source nature, the Android operating system has consistently been a primary target for attackers. Learning-based methods have made significant progress in the field of Android malware detection. However, traditional…

With the rapid growth of Android malware, many machine learning-based malware analysis approaches are proposed to mitigate the severe phenomenon. However, such classifiers are opaque, non-intuitive, and difficult for analysts to understand…

The presence and persistence of Android malware is an on-going threat that plagues this information era, and machine learning technologies are now extensively used to deploy more effective detectors that can block the majority of these…

Android is the most widely used smartphone OS with 82.8% market share in 2015. It is therefore the most widely targeted system by malware authors. Researchers rely on dynamic analysis to extract malware behaviors and often use emulators to…

Widespread growth in Android malwares stimulates security researchers to propose different methods for analyzing and detecting malicious behaviors in applications. Nevertheless, current solutions are ill-suited to extract the fine-grained…

Deep learning has emerged as a promising technology for achieving Android malware detection. To further unleash its detection potentials, software visualization can be integrated for analyzing the details of app behaviors clearly. However,…

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,…

The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle to keep pace with emerging malware variants that employ…

Android is undergoing unprecedented malicious threats daily, but the existing methods for malware detection often fail to cope with evolving camouflage in malware. To address this issue, we present HAWK, a new malware detection framework…

The use of machine learning and intelligent systems has become an established practice in the realm of malware detection and cyber threat prevention. In an environment characterized by widespread accessibility and big data, the feasibility…

The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and…

Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them,…

Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these…

With the increasing popularity of Android in the last decade, Android is popular among users as well as attackers. The vast number of android users grabs the attention of attackers on android. Due to the continuous evolution of the variety…

Malware detection is a constant challenge in cybersecurity due to the rapid development of new attack techniques. Traditional signature-based approaches struggle to keep pace with the sheer volume of malware samples. Machine learning offers…

As malware continues to become more complex and harder to detect, Malware Analysis needs to continue to evolve to stay one step ahead. One promising key area approach focuses on using system calls and API Calls, the core communication…

Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis…

In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown…

Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study the susceptibility of feature-based ML malware classifiers…

In recent years, the rise of cyber threats has emphasized the need for robust malware detection systems, especially on mobile devices. Malware, which targets vulnerabilities in devices and user data, represents a substantial security risk.…