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Security Orchestration, Automation, and Response (SOAR) platforms integrateand orchestrate a wide variety of security tools to accelerate the operationalactivities of Security Operation Center (SOC). Integration of security tools ina SOAR platform is mostly done manually using APIs, plugins, and scripts. SOCteams need to navigate through API calls of different security tools to find asuitable API to define or update an incident response action. Analyzing varioustypes of API documentation with diverse API format and presentation structureinvolves significant challenges such as data availability, data heterogeneity,and semantic variation for automatic identification of security tool APIsspecific to a particular task. Given these challenges can have negative impacton SOC team's ability to handle security incident effectively and efficiently,we consider it important to devise suitable automated support solutions toaddress these challenges. We propose a novel learning-based framework forautomated security tool API Recommendation for security Orchestration,automation, and response, APIRO. To mitigate data availability constraint,APIRO enriches security tool API description by applying a wide variety of dataaugmentation techniques. To learn data heterogeneity of the security tools andsemantic variation in API descriptions, APIRO consists of an API-specific wordembedding model and a Convolutional Neural Network (CNN) model that are usedfor prediction of top 3 relevant APIs for a task. We experimentally demonstratethe effectiveness of APIRO in recommending APIs for different tasks using 3security tools and 36 augmentation techniques. Our experimental resultsdemonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy.

APIRO: A Framework for Automated Security Tools API Recommendation