Related papers: Detecting Android Malware: From Neural Embeddings …
Malware, or software designed with harmful intent, is an ever-evolving threat that can have drastic effects on both individuals and institutions. Neural network malware classification systems are key tools for combating these threats but…
The amount of Android malware has increased greatly during the last few years. Static analysis is widely used in detecting such malware by analyzing the code without execution. The effectiveness of current tools relies on the app model as…
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…
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…
Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of…
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…
Copious mobile operating systems exist in the market, but Android remains the user's choice. Meanwhile, its growing popularity has also attracted malware developers. Researchers have proposed various static solutions for Android malware…
Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying costs and benefits. Malware naturally exhibits all of these complexities, but for the same…
Malware detection is a critical aspect of information security. One difficulty that arises is that malware often evolves over time. To maintain effective malware detection, it is necessary to determine when malware evolution has occurred so…
Malware remains a big threat to cyber security, calling for machine learning based malware detection. While promising, such detectors are known to be vulnerable to evasion attacks. Ensemble learning typically facilitates countermeasures,…
Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time. In this paper, we propose Droidetec, a deep…
The existing malware classification approaches (i.e., binary and family classification) can barely benefit subsequent analysis with their outputs. Even the family classification approaches suffer from lacking a formal naming standard and an…
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…
Smartphones contain information that is more sensitive and personal than those found on computers and laptops. With an increase in the versatility of smartphone functionality, more data has become vulnerable and exposed to attackers.…
Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more.…
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…
Malware analysis involves analyzing suspicious software to detect malicious payloads. Static malware analysis, which does not require software execution, relies increasingly on machine learning techniques to achieve scalability. Although…
Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Thus, malware identification enables security researchers and incident responders to take precautions…
The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature…
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…