SmartValidator: A Framework for Automatic Identification and Classification of Cyber Threat Data

TL;DR

SmartValidator is an AI-driven framework that automates the classification and validation of cyber threat data, reducing manual effort and response time for security operations centers by dynamically building effective models.

Contribution

It introduces a novel AI-based framework that automatically constructs tailored ML models for cyber threat data validation, improving efficiency and adaptability over manual rule-based methods.

Findings

75% of models achieved F1-score above 0.8

Dynamic model construction reduces the number of models needed by 99%

Framework effectively accelerates alert validation in real-world settings

Abstract

A wide variety of Cyber Threat Information (CTI) is used by Security Operation Centres (SOCs) to perform validation of security incidents and alerts. Security experts manually define different types of rules and scripts based on CTI to perform validation tasks. These rules and scripts need to be updated continuously due to evolving threats, changing SOCs' requirements and dynamic nature of CTI. The manual process of updating rules and scripts delays the response to attacks. To reduce the burden of human experts and accelerate response, we propose a novel Artificial Intelligence (AI) based framework, SmartValidator. SmartValidator leverages Machine Learning (ML) techniques to enable automated validation of alerts. It consists of three layers to perform the tasks of data collection, model building and alert validation. It projects the validation task as a classification problem. Instead of building and saving models for all possible requirements, we propose to automatically construct the validation models based on SOC's requirements and CTI. We built a Proof of Concept (PoC) system with eight ML algorithms, two feature engineering techniques and 18 requirements to investigate the effectiveness and efficiency of SmartValidator. The evaluation results showed that when prediction models were built automatically for classifying cyber threat data, the F1-score of 75\% of the models were above 0.8, which indicates adequate performance of the PoC for use in a real-world organization. The results further showed that dynamic construction of prediction models required 99\% less models to be built than pre-building models for all possible requirements. The framework can be followed by various industries to accelerate and automate the validation of alerts and incidents based on their CTI and SOC's preferences.