NLP

Our new Cyber-Physical Security Assurance Framework based on Semi-Supervised Vetting applies the latest AI/ML and NLP technologies on hardware testbeds to advance the resilience of critical cyber-physical assets including electric grids and gas pipelines.

This paper presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. The verification pipeline includes automated vendor identification, device document curation, and feature claim identification via sentiment analysis. Our novel matching engine represents the first automated information system available in the cybersecurity domain to directly aid ICS compliance reporting.

Our classifier of feature claims for cybersecurity literature analytics is introduced in our new model called ClaimsBERT. ClaimsBERT outperforms all other evaluated framework aiming to improve the cybersecurity of industrial control systems (ICS).

We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS)…The results showed that CyBERT outperforms these models on the validation accuracy and the F1 score, validating CyBERT’s robustness and accuracy as a cybersecurity feature claims classifier.

Cyber-physical systems span a wide spectrum, from long-lived legacy systems to more modern installations. Trust is an issue that arises across the spectrum, albeit with different variants of goals and constraints. On the one end of the spectrum, legacy systems are characterized by function-based designs in which trust is an implicitly in-built concept…

A solution is needed for vetting the vendor-supplied feature claims and their adherence to cybersecurity requirements and standards. We are presently engaged in an effort to develop such a system. This paper demonstrates one vital aspect of this effort in proposing an end-to-end framework to accumulate a large repository of ICS device information for this vetting system, curate the dataset, and conduct extensive processing. This framework is designed to use web scraping, data analytics and Natural Language Processing (NLP) techniques to identify vendor websites, automate the collection of website-accessible documents and automatically derive metadata from them for identification of product documents relevant to the repository…

The cybersecurity auditing for Operation Technology is critical and has been largely missing from the cybersecurity research, especially in the energy sector. In this paper, we present a novel “cybersecurity vetting” approach (CYVET) to the problem of verification and validation of cybersecurity in complex cyber-physical installations underlying modern energy grid systems.