Machine Learning Sentiment Analysis in Cyber Threat Intelligence Recommendation System
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Abstract
The use of the digital world is increasing every day. Attacks and data theft occur on various websites, both government-owned and commercial and banking sites. Therefore, this research aims to identify the threats of frequently occurring viruses in a country. There is a considerable amount of news explaining cybercrime incidents. The problem of this research is that unstructured data such as articles and technical reports are difficult to analyze and identify the types of cybercrime attacks. Previous research attempted to semantically extract unstructured cyber threats, but there were shortcomings in previous research. The novelty of this research is the development of a Cyber Threat Intelligence (CTI) machine learning model to identify the types of virus attacks or cybercrimes that frequently occur in e-commerce transactions, so that they can take rescue actions for incident handling in the digital world using tactics, techniques, and procedures (TTP). The method involves using machine learning, taking Cyber Threat Intelligence (CTI) documents as input regarding cybersecurity threat handling steps, and then processing the data using AI TF-IDF and Bags of Words for the identification of steps, tactics, techniques, and procedures required for each frequently occurring security incident.
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