Published Paper Details:

This paper focuses on analyzing the LockBit Black ransomware, a recent and potent threat that has particularly targeted the healthcare sector. As ransomware continues to evolve and combine various malicious functionalities, it becomes crucial to detect its patterns of infection and persistence methods to prevent data, system, and network contamination. By identifying these infections early, containment can be achieved, and further spread can be avoided. The paper discusses reports and data leaks highlighting LockBit Black's significant contributions to attacks. The method chapter explores different approaches to detecting ransomware, such as static, dynamic, and network-based methods. The paper then describes the specific approach taken in this study, including the tools utilized. The results and discussion section presents the creation of two sandbox environments for manual analysis and the corresponding findings. Additionally, the paper introduces five free online malware analysis platforms, whose reports were compared with the manual results. The IoCs (Indicators of Compromise) identified during the analysis are provided, enabling other researchers to create signatures for early detection, prevention, and the recording of forensic evidence. This study aims to analyze LockBit Black ransomware comprehensively and identify its footprints to serve as a valuable resource.