An automated method for the ontological representation of security
directives
- URL: http://arxiv.org/abs/2307.01211v1
- Date: Fri, 30 Jun 2023 09:04:47 GMT
- Title: An automated method for the ontological representation of security
directives
- Authors: Giampaolo Bella, Gianpietro Castiglione, Daniele Francesco Santamaria
- Abstract summary: The paper frames this problem in the context of recent European security directives.
The complexity of their language is here thwarted by the extraction of the relevant information, namely of the parts of speech from each clause.
The method is showcased on a practical problem, namely to derive an ontology representing the NIS 2 directive, which is the peak of cybersecurity prescripts at the European level.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Large documents written in juridical language are difficult to interpret,
with long sentences leading to intricate and intertwined relations between the
nouns. The present paper frames this problem in the context of recent European
security directives. The complexity of their language is here thwarted by
automating the extraction of the relevant information, namely of the parts of
speech from each clause, through a specific tailoring of Natural Language
Processing (NLP) techniques. These contribute, in combination with ontology
development principles, to the design of our automated method for the
representation of security directives as ontologies. The method is showcased on
a practical problem, namely to derive an ontology representing the NIS 2
directive, which is the peak of cybersecurity prescripts at the European level.
Although the NLP techniques adopted showed some limitations and had to be
complemented by manual analysis, the overall results provide valid support for
directive compliance in general and for ontology development in particular.
Related papers
- Comparing Feature-based and Context-aware Approaches to PII Generalization Level Prediction [0.6138671548064356]
PII in text data is crucial for privacy, but current generalization methods face challenges such as uneven data distributions and limited context awareness.
We propose two approaches: a feature-based method using machine learning to improve performance on structured inputs, and a novel context-aware framework that considers the broader context and semantic relationships between the original text and generalized candidates.
Experiments on the WikiReplace dataset demonstrate the effectiveness of both methods, with the context-aware approach outperforming the feature-based one across different scales.
arXiv Detail & Related papers (2024-07-03T06:32:03Z) - Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation [5.646219481667151]
This paper introduces a novel dataset tailored for classification of statements made during police interviews, prior to court proceedings.
We introduce a fine-tuned DistilBERT model that achieves state-of-the-art performance in distinguishing truthful from deceptive statements.
We also present an XAI interface that empowers both legal professionals and non-specialists to interact with and benefit from our system.
arXiv Detail & Related papers (2024-05-17T11:22:27Z) - Cross-domain Chinese Sentence Pattern Parsing [67.1381983012038]
Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.
Existing SPSs rely heavily on textbook corpora for training, lacking cross-domain capability.
This paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework.
arXiv Detail & Related papers (2024-02-26T05:30:48Z) - Instruct2Attack: Language-Guided Semantic Adversarial Attacks [76.83548867066561]
Instruct2Attack (I2A) is a language-guided semantic attack that generates meaningful perturbations according to free-form language instructions.
We make use of state-of-the-art latent diffusion models, where we adversarially guide the reverse diffusion process to search for an adversarial latent code conditioned on the input image and text instruction.
We show that I2A can successfully break state-of-the-art deep neural networks even under strong adversarial defenses.
arXiv Detail & Related papers (2023-11-27T05:35:49Z) - Learning Symbolic Rules over Abstract Meaning Representations for
Textual Reinforcement Learning [63.148199057487226]
We propose a modular, NEuroSymbolic Textual Agent (NESTA) that combines a generic semantic generalization with a rule induction system to learn interpretable rules as policies.
Our experiments show that the proposed NESTA method outperforms deep reinforcement learning-based techniques by achieving better to unseen test games and learning from fewer training interactions.
arXiv Detail & Related papers (2023-07-05T23:21:05Z) - An Ontological Approach to Compliance Verification of the NIS 2 Directive [0.0]
This paper introduces an approach that leverages techniques of semantic representation and reasoning, hence an ontological approach, towards the compliance check with the security measures that textual documents prescribe.
The formalisation of entities and relations from the directive, and the consequent improved structuring with respect to sheer prose is dramatically helpful for any organisation through the hard task of compliance verification.
arXiv Detail & Related papers (2023-06-30T09:10:54Z) - Towards Grammatical Tagging for the Legal Language of Cybersecurity [0.0]
Legal language can be understood as the language typically used by those engaged in the legal profession.
Recent legislation on cybersecurity obviously uses legal language in writing.
This paper faces the challenge of the essential interpretation of the legal language of cybersecurity.
arXiv Detail & Related papers (2023-06-29T15:39:20Z) - Guiding the PLMs with Semantic Anchors as Intermediate Supervision:
Towards Interpretable Semantic Parsing [57.11806632758607]
We propose to incorporate the current pretrained language models with a hierarchical decoder network.
By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks.
We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines.
arXiv Detail & Related papers (2022-10-04T07:27:29Z) - The Whole Truth and Nothing But the Truth: Faithful and Controllable
Dialogue Response Generation with Dataflow Transduction and Constrained
Decoding [65.34601470417967]
We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation.
Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
arXiv Detail & Related papers (2022-09-16T09:00:49Z) - Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech [27.657676278734534]
This paper proposes a methodology for constructing such corpora of child directed speech paired with sentential logical forms.
The approach enforces a cross-linguistically consistent representation, building on recent advances in dependency representation and semantic parsing.
arXiv Detail & Related papers (2021-09-22T18:17:06Z) - PIN: A Novel Parallel Interactive Network for Spoken Language
Understanding [68.53121591998483]
In the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them.
The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach.
More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.
arXiv Detail & Related papers (2020-09-28T15:59:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.