SIREN - Security Information Retrieval and Extraction eNgine

Sanagavarapu, Lalit Mohan; Mathur, N. N.; Agrawal, Shraddha; Reddy, Y. Raghu

doi:10.1007/978-3-319-76941-7_81

Cited by 1 publication

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“…Given the extent of effort and cost involved, access to domain content and ability to process text with advanced natural language processing (NLP) techniques and ML models on powerful IT infrastructure opens up research opportunities to construct and manage ontologies. The information security ontologies can be constructed or enriched from unstructured text available on public forums, vulnerability databases such as National Vulnerability Database (NVD) 3 and other information security processing systems [7], [8] sources. Also, standards and guidelines from ISO/IEC [9], NIST from US, ENISA from European Nation, Cloud Security Alliance (CSA) and others to protect confidentiality, integrity and availability of IT assets, contain embedded concepts.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Ontology Enrichment from Unstructured Text

Sanagavarapu¹,

Iyer²,

Reddy³

2021

Preprint

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Information Security in the cyber world is a major cause for concern, with significant increase in the number of attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on web provides an opportunity to represent knowledge and perform security analytics to mitigate some of the concerns. Representing security knowledge in the form of ontology facilitates anomaly detection, threat intelligence, reasoning and relevance attribution of attacks, and many more. This necessitates dynamic and automated enrichment of information security ontologies. However, existing ontology enrichment algorithms based on natural language processing and ML models have issues with contextual extraction of concepts in words, phrases and sentences. This motivates the need for sequential Deep Learning architectures that traverse through dependency paths in text and extract embedded vulnerabilities, threats, controls, products and other security related concepts and instances from learned path representations. In the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder is deployed to enrich ISO 27001 [1] based information security ontology. The model is trained and tested on an high performance computing (HPC) environment to handle Wiki text dimensionality. The approach yielded a test accuracy of over 80% when tested with knocked out concepts from ontology and web page instances to validate the robustness.

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Section: Introductionmentioning

confidence: 99%