相关论文: FCGHunter: Towards Evaluating Robustness of Graph-…
The Android operating system is the most spread mobile platform in the world. Therefor attackers are producing an incredible number of malware applications for Android. Our aim is to detect Android's malware in order to protect the user. To…
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…
Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor…
The emergence of mobile platforms with increased storage and computing capabilities and the pervasive use of these platforms for sensitive applications such as online banking, e-commerce and the storage of sensitive information on these…
Software vulnerability detection is crucial for high-quality software development. Recently, some studies utilizing Graph Neural Networks (GNNs) to learn the graph representation of code in vulnerability detection tasks have achieved…
Graph Neural Networks (GNNs) have achieved notable success in tasks such as social and transportation networks. However, recent studies have highlighted the vulnerability of GNNs to backdoor attacks, raising significant concerns about their…
The Android operating system is the most spread mobile platform in the world. Therefor attackers are producing an incredible number of malware applications for Android. Our aim is to detect Android's malware in order to protect the user. To…
Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the…
With the number of new mobile malware instances increasing by over 50\% annually since 2012 [24], malware embedding in mobile apps is arguably one of the most serious security issues mobile platforms are exposed to. While obfuscation…
Machine learning-based malware detection dominates current security defense approaches for Android apps. However, due to the evolution of Android platforms and malware, existing such techniques are widely limited by their need for constant…
Android malware detection systems suffer severe performance degradation over time due to concept drift caused by evolving malicious and benign app behaviors. Although recent methods leverage active learning and hierarchical contrastive loss…
Android malware is a persistent threat to billions of users around the world. As a countermeasure, Android malware detection systems are occasionally implemented. However, these systems are often vulnerable to \emph{evasion attacks}, in…
Anti-analysis techniques, particularly packing, challenge malware analysts, making packer identification fundamental. Existing packer identifiers have significant limitations: signature-based methods lack flexibility and struggle against…
Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, in which an adversary…
In this paper, we propose a novel graph kernel specifically to address a challenging problem in the field of cyber-security, namely, malware detection. Previous research has revealed the following: (1) Graph representations of programs are…
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded by designated rule-based detectors that heavily rely on…
As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns.…
The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting. To address this issue, we propose an enhanced Cypider framework, a set…
With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patterns from Android apps. However, the lack of an in-depth and…
Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers and defenders. However, the strategies behind both sides…