Related papers: Avast-CTU Public CAPE Dataset

Deep research systems represent an emerging class of agentic information retrieval methods that generate comprehensive and well-supported reports to complex queries. However, most existing frameworks rely on dynamic commercial search APIs,…

Malware family labels are known to be inconsistent. They are also black-box since they do not represent the capabilities of malware. The current state-of-the-art in malware capability assessment include mostly manual approaches, which are…

A key feature of collaboration in science and software development is to have a {\em log} of what and how is being done - for private use and reuse and for sharing selected parts with collaborators, which most often today are distributed…

In cloud-based endpoint auditing, security administrators often rely on the cloud to perform causality analysis over log-derived versioned provenance graphs to investigate suspicious attack behaviors. However, the cloud may be distrusted or…

Malware detection is a ubiquitous application of Machine Learning (ML) in security. In behavioral malware analysis, the detector relies on features extracted from program execution traces. The research literature has focused on detectors…

The lack of data sets derived from operational enterprise networks continues to be a critical deficiency in the cyber security research community. Unfortunately, releasing viable data sets to the larger com- munity is challenging for a…

With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders,…

Being on a mushrooming spree since at least 2013, malware can take a large toll on any system. In a perpetual cat-and-mouse chase with defenders, malware writers constantly conjure new methods to hide their code so as to evade detection by…

Artificial intelligence (AI) systems are increasingly adopted as tool-using agents that can plan, observe their environment, and take actions over extended time periods. This evolution challenges current evaluation practices where the AI…

Malware is becoming increasingly complex and widespread, making it essential to develop more effective and timely detection methods. Traditional static analysis often fails to defend against modern threats that employ code obfuscation,…

In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing…

Artificial intelligence and machine learning have significantly advanced malware research by enabling automated threat detection and behavior analysis. However, the availability of exploitable data is limited, due to the absence of large…

The Cyber world is plagued with ever-evolving malware that readily infiltrates all defense mechanisms, operates viciously unbeknownst to the user and surreptitiously exfiltrate sensitive data. Understanding the inner workings of such…

The continuous increase in malware samples, both in sophistication and number, presents many challenges for organizations and analysts, who must cope with thousands of new heterogeneous samples daily. This requires robust methods to quickly…

Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their threat model, shifting the responsibility of mitigating such…

Despite the potential of Machine learning (ML) to learn the behavior of malware, detect novel malware samples, and significantly improve information security (InfoSec) we see few, if any, high-impact ML techniques in deployed systems,…

The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a…

Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly.…

In the modern era, malware is experiencing a significant increase in both its variety and quantity, aligning with the widespread adoption of the digital world. This surge in malware has emerged as a critical challenge in the realm of…

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…