NEAT: A Nile-English Aligned Translation Corpus based on a Robust Methodology for Intent Based Networking and Security

The rise of Intent Based Networking (IBN) has paved the way for more efficient network and security management, reduced errors, and accelerated deployment times by leveraging AI processes capable of translating natural language intents into policies or configurations. Specialized neural networks could offer a promising solution at the core of translation operations. Still, they require dedicated, large-scale corpora for training generative models, which are not currently available due to the restricted and confidential nature of the information they convey. This talk fills this gap by proposing a novel methodology for creating a corpus specifically tailored to train a Large Language Model (LLM) for this translation task. Our approach leverages advancements in Natural Language Processing (NLP) to overcome the challenges posed by the limited training data, enabling accurate interpretation and translation of high-level performance and security directives from natural language into structured, actionable formats, represented by the intermediate Network Intent LanguagE (Nile). Our experimental evaluations, grounded by human experts, indicate that our corpus generation methodology yields promising results in terms of translation accuracy, language naturalness, and reliability. To further ensure the correctness of the produced translations, our evaluation framework compares and classifies LLM outputs, assessing their semantic fidelity. This process minimizes the risk of incorrect or unsafe intent translations by filtering out translations that fail to meet stringent accuracy criteria. As a result, we have been able to generate Nile-English Aligned Translations (NEAT), a corpus which is to date two orders of magnitude larger than currently available datasets, exhibits a wide coverage of the Nile syntax, and has much lower perplexity values than other generated corpora. NEAT has been made publicly available to facilitate further research and development in adapting generative models to network management and security.