相关论文: SeqMobile: A Sequence Based Efficient Android Malw…

Sophisticated evasion tactics in malicious Android applications, combined with their intricate behavioral semantics, enable attackers to conceal malicious logic within legitimate functions, underscoring the critical need for robust and…

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and…

Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to simplify ML development by reducing the need for specialized…

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,…

The explosive growth and increasing sophistication of Android malware call for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming…

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…

One of the pivotal security threats for the embedded computing systems is malicious software a.k.a malware. With efficiency and efficacy, Machine Learning (ML) has been widely adopted for malware detection in recent times. Despite being…

Malware detection is an ever-present challenge for all organizational gatekeepers, who must maintain high detection rates while minimizing interruptions to the organization's workflow. To improve detection rates, organizations often deploy…

Mobile devices have evolved from just communication devices into an indispensable part of people's lives in form of smartphones, tablets and smart watches. Devices are now more personal than ever and carry more information about a person…

Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment and logging their actions. Previous work has proposed training machine learning models, i.e., convolutional and long short-term memory…

We investigate the use of Android permissions as the vehicle to allow for quick and effective differentiation between benign and malware apps. To this end, we extract all Android permissions, eliminating those that have zero impact, and…

In evaluating detection methods, the malware research community relies on scan results obtained from online platforms such as VirusTotal. Nevertheless, given the lack of standards on how to interpret the obtained data to label apps,…

Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine…

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…

The astonishing spread of Android OS, not only in smartphones and tablets but also in IoT devices, makes this operating system a very tempting target for malware threats. Indeed, the latter are expanding at a similar rate. In this respect,…

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…

We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs (QDFGs) model process behavior by interpreting issued system calls as aggregations of quantifiable data…

There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue…

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…

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…