  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.

Cryptography and Security;Software Engineering