Actionable Cyber Threat Intelligence: An Overview and Framework

Abstract

Cyber-attack sophistication is on a definite rise; proactive security strategies need to keep up. In this respect, actionable Cyber Threat Intelligence is of paramount importance for identification, analysis, and mitigation of threats. This paper presents the existing methods of Cyber Threat Intelligence and proposes a new architecture that enhances the actionable capability of threat intelligence with artificial intelligence. It uses machine learning algorithms, Natural Language Processing, and Data Analytics to turn raw data into Actionable Insights, thereby allowing a business to take an informed decision and enhance cybersecurity. The tactical and operational cyber threat intelligence is the focus, emphasizing tactics, techniques, and procedures (TTPs) that form the foundation of information necessary to exploit a particular organization’s network strengths and weaknesses. In this process, this paper surveys a variety of methodologies in use in cyber threat intelligence and underlines the role of threat information in proactive cybersecurity, something very critical in threat and vulnerability reduction. It provides insight into entry points, tactics of threat actors, and system vulnerabilities. However, in such a large and unstructured areas the task of cybersecurity intelligence becomes demanding. Tactical and operational threat intelligence helps in structuring the bulk of data in unison and turning it into action-based insights, hence enhancing security postures. Adequate machine learning technology is crucial to deal with such huge data volumes and speed up the time-consuming process of gathering actionable intelligence since it is not possible to sort through it manually in a field like cybersecurity that is changing by the minute.

Keywords: Advanced Persistent Threats (APT), Artificial Intelligence (AI), Cyber Threat Intelligence (CTI), Indicator of Compromise (IoC), Internet of Things (IoT), Machine Learning (ML)

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