| Abstract [eng] |
The landscape of malware has changed drastically in recent years, with increasingly sophisticated threats emerging continually. The most concerning are those used for espionage and advanced malware campaigns, notably advanced persistent threats, they are designed to infiltrate and linger undetected in systems for prolonged periods, often evading traditional detection methods like signature-based systems. This presents a significant challenge as it necessitates the development of more advanced detection strategies and methodologies. As cybersecurity defenses evolve, so too do the tactics of malware creators, leading to an arms race between defensive measures and malicious innovations. This dynamic has amplified the need for approaches to malware detection, as well as comprehensive datasets for testing and refining these methods. To address this need, this research discusses the development of a simulator model and an actual simulator designed and developed to simulate and gather malicious behavior. This software aims to fill the critical gap in resources for simulating the sophisticated activities of malware, which can be used in training advanced detection methods. This dataset is crucial for researchers aiming to improve anomaly detection and behavior-based detection technologies. The evaluation of the simulator confirmed its ability in replicating complex malware behaviors. |
