| Popis: |
In cybersecurity, adversaries employ a myriad of tactics to evade detection and breach defenses. Malware remains a formidable weapon in their arsenal. To counter this threat, researchers unceasingly pursue dynamic analysis, which aims to comprehend and thwart established malware strains. This paper introduces an innovative methodology for dynamic malware analysis while critically evaluating prevailing technologies and their limitations. The proposed approach hinges on harnessing the capabilities of an open-source Security Information and Event Management (SIEM) toolset, namely the Elastic-Stack. This toolset is utilized to capture, structure, and analyze the behavioral patterns of malware without relying on any pre-existing sandbox framework. This augmentation facilitates a profound understanding of the activities exhibited by malware samples. With the help of the proposed ecosystem, we compile the AAU_MalData dataset that encompasses distinctly the benign and malicious behavior. Specifically, we analyzed the behavior of the 2,800 malware within a realistic network topology and systematically collected Windows event logs, which serve as a comprehensive record of the malware’s actions. These event logs are precious as they are organized in a timestamped format, providing a chronological list of system activities, such as event descriptions, process and file details, and registry modifications. These are pivotal in comprehending the malware’s functionality and repercussions on the compromised system. Furthermore, by incorporating the MITRE ATT&CK framework, we leveraged the event logs to correlate the malware’s mode of operation, delve into its Command and Control operations, and investigate its persistence mechanisms, enabling a structured and practical approach to malware analysis. The AAU_MalData dataset, organized in a JSON format data structure, is offered to the research community first as a proof of concept to demonstrate the toolset’s feasibility for dynamic malware analysis and second as a potential training ground for anti-malware mechanisms based on host and network Indicators of Compromise (IoCs). |
