RITA: Automatic Framework for Designing of Resilient IoT Applications

TL;DR

RITA is an automated, offline framework that uses a fine-tuned RoBERTa model to identify critical IoT objects, analyze threats, and recommend mitigation strategies, improving resilience in IoT system design.

Contribution

It introduces RITA, an open-source, offline tool that automates IoT resilience design tasks, outperforming ChatGPT in key object identification categories.

Findings

RITA outperforms ChatGPT in 4 of 7 ICO categories.

RITA operates entirely offline, ensuring data privacy.

RITA enhances standardization and consistency in IoT resilience design.

Abstract

Designing resilient Internet of Things (IoT) systems requires i) identification of IoT Critical Objects (ICOs) such as services, devices, and resources, ii) threat analysis, and iii) mitigation strategy selection. However, the traditional process for designing resilient IoT systems is still manual, leading to inefficiencies and increased risks. In addition, while tools such as ChatGPT could support this manual and highly error-prone process, their use raises concerns over data privacy, inconsistent outputs, and internet dependence. Therefore, we propose RITA, an automated, open-source framework that uses a fine-tuned RoBERTa-based Named Entity Recognition (NER) model to identify ICOs from IoT requirement documents, correlate threats, and recommend countermeasures. RITA operates entirely offline and can be deployed on-site, safeguarding sensitive information and delivering consistent outputs that enhance standardization. In our empirical evaluation, RITA outperformed ChatGPT in four of seven ICO categories, particularly in actuator, sensor, network resource, and service identification, using both human-annotated and ChatGPT-generated test data. These findings indicate that RITA can improve resilient IoT design by effectively supporting key security operations, offering a practical solution for developing robust IoT architectures.