Framework for Extraction of Tactical Threat Intelligence from Cyber Threat Reports

Abstract

Cybersecurity threats have become a major concern for organizations in recent years. With the rapid advancement of technology, cybercriminals have also become more sophisticated in their tactics, techniques, and procedures (TTPs) to breach the defenses of organizations. These attacks have grown in number and complexity, and they can come in many forms, such as phishing, malware, ransomware, and social engineering. To make matters worse, traditional security solutions are often unable to keep pace with the constantly evolving threat landscape. Cybercriminals are quick to adapt to new security measures and find new ways to evade detection, which makes it even more difficult for organizations to defend against cyber-attacks. In response to this growing challenge, the cybersecurity community has recognized the importance of sharing cyber threat intelligence (CTI) to identify and respond to attacks in a timely and cost-effective manner. CTI refers to the information that is collected, analyzed, and shared about potential or actual cyber threats. This information includes details about the tactics, techniques, and procedures (TTPs) used by cybercriminals, as well as the tools and infrastructure they employ to carry out their attacks. CTI refers to the knowledge, information, and data that are gathered, analyzed, and shared to detect, prevent, and respond to cyber threats. By sharing CTI, organizations can gain valuable insights into emerging threats and vulnerabilities and receive early warning of potential attacks. Sharing CTI also helps organizations to better understand the motives and tactics of cybercriminals. This understanding is crucial in developing effective security measures that can prevent cyber-attacks. Through the analysis of CTI, organizations can learn about the techniques used by cybercriminals and the tools they employ to gain unauthorized access to systems and data. This knowledge can be used to develop more effective security strategies that are better suited to combat the latest trends in cybercrime. However, manual analysis of complex and unstructured CTI reports is a time-consuming and labor-intensive process that requires specialized skills and expertise. The sheer volume of data available can be overwhelming, and the information gathered may be fragmented and inconsistent, making it difficult to identify relevant information. Therefore, organizations should consider using automated tools and techniques to assist in the analysis of CTI reports. These tools can help to streamline the process of CTI analysis. To overcome these challenges, our paper proposes an innovative approach that combines enhanced techniques of Natural Language Processing (NLP) and Information Retrieval (IR) to develop automated and context-aware analytics of cyber threat intelligence. Our approach can accurately learn attack patterns (TTPs) from commonly available CTI sources, enabling organizations to implement timely and effective cyber defense actions. This research makes three key contributions to the field of cybersecurity. First, it presents a novel threat-action ontology that is sufficiently rich to understand the specifications and context of malicious actions. Second, it develops a novel text mining approach that can extract threat actions from CTI reports based on semantic relationships. Third, the CTI analysis can construct a complete attack pattern by mapping each threat action to the appropriate techniques, tactics, and kill chain phases, and translating it into any threat sharing standards, such as STIX 2.1. By automating the analysis of CTI reports, the presented framework can significantly reduce the time and effort required to identify and respond to cyber-attacks. This research provides a significant contribution to the field of cybersecurity and has the potential to enhance the effectiveness of CTI sharing, ultimately improving the overall security posture of organizations.