A Threat Intelligence Event Extraction Conceptual Model for Cyber Threat Intelligence Feeds

• URL: http://arxiv.org/abs/2506.03551v1
• Date: Wed, 04 Jun 2025 04:09:01 GMT
• Title: A Threat Intelligence Event Extraction Conceptual Model for Cyber Threat Intelligence Feeds
• Authors: Jamal H. Al-Yasiri, Mohamad Fadli Bin Zolkipli, Nik Fatinah N Mohd Farid, Mohammed Alsamman, Zainab Ali Mohammed,
• Abstract summary: The efficiency of Cyber Threat Intelligence (CTI) data collection has become paramount in ensuring robust cybersecurity.<n>Existing works encounter significant challenges in preprocessing large volumes of multilingual threat data, leading to inefficiencies in real-time threat analysis.<n>This paper presents a systematic review of current techniques aimed at enhancing CTI data collection efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In response to the escalating cyber threats, the efficiency of Cyber Threat Intelligence (CTI) data collection has become paramount in ensuring robust cybersecurity. However, existing works encounter significant challenges in preprocessing large volumes of multilingual threat data, leading to inefficiencies in real-time threat analysis. This paper presents a systematic review of current techniques aimed at enhancing CTI data collection efficiency. Additionally, it proposes a conceptual model to further advance the effectiveness of threat intelligence feeds. Following the PRISMA guidelines, the review examines relevant studies from the Scopus database, highlighting the critical role of artificial intelligence (AI) and machine learning models in optimizing CTI data preprocessing. The findings underscore the importance of AI-driven methods, particularly supervised and unsupervised learning, in significantly improving the accuracy of threat detection and event extraction, thereby strengthening cybersecurity. Furthermore, the study identifies a gap in the existing research and introduces XBC conceptual model integrating XLM-RoBERTa, BiGRU, and CRF, specifically developed to address this gap. This paper contributes conceptually to the field by providing a detailed analysis of current CTI data collection techniques and introducing an innovative conceptual model to enhance future threat intelligence capabilities.

Related papers

• A Survey on Model Extraction Attacks and Defenses for Large Language Models [55.60375624503877]
  Model extraction attacks pose significant security threats to deployed language models.<n>This survey provides a comprehensive taxonomy of extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks.<n>We examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios.
  arXiv  Detail & Related papers  (2025-06-26T22:02:01Z)
• Integrating Graph Theoretical Approaches in Cybersecurity Education CSCI-RTED [0.0]
  Graph theory offers a powerful framework for modeling relationships within cyber ecosystems.<n>This paper develops an enriched version of the widely recognized NSL-KDD dataset, incorporating graph-theoretical concepts to enhance its practical value.
  arXiv  Detail & Related papers  (2025-04-23T19:08:30Z)
• Cyber Defense Reinvented: Large Language Models as Threat Intelligence Copilots [36.809323735351825]
  CYLENS is a cyber threat intelligence copilot powered by large language models (LLMs)<n>CYLENS is designed to assist security professionals throughout the entire threat management lifecycle.<n>It supports threat attribution, contextualization, detection, correlation, prioritization, and remediation.
  arXiv  Detail & Related papers  (2025-02-28T07:16:09Z)
• Beyond the Surface: An NLP-based Methodology to Automatically Estimate CVE Relevance for CAPEC Attack Patterns [42.63501759921809]
  We propose a methodology leveraging Natural Language Processing (NLP) to associate Common Vulnerabilities and Exposure (CAPEC) vulnerabilities with Common Attack Patternion and Classification (CAPEC) attack patterns.<n> Experimental evaluations demonstrate superior performance compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2025-01-13T08:39:52Z)
• CTINexus: Automatic Cyber Threat Intelligence Knowledge Graph Construction Using Large Language Models [49.657358248788945]
  Textual descriptions in cyber threat intelligence (CTI) reports are rich sources of knowledge about cyber threats.<n>Current CTI knowledge extraction methods lack flexibility and generalizability.<n>We propose CTINexus, a novel framework for data-efficient CTI knowledge extraction and high-quality cybersecurity knowledge graph (CSKG) construction.
  arXiv  Detail & Related papers  (2024-10-28T14:18:32Z)
• Countering Autonomous Cyber Threats [40.00865970939829]
  Foundation Models present dual-use concerns broadly and within the cyber domain specifically. Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations. This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
  arXiv  Detail & Related papers  (2024-10-23T22:46:44Z)
• Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects [0.33554367023486936]
  This paper provides a comprehensive review of machine learning-based Network Intrusion Detection Systems (NIDS) We critically examine existing research in NIDS, highlighting key trends, strengths, and limitations. We discuss emerging challenges in the field and offer insights for the development of more robust and resilient NIDS.
  arXiv  Detail & Related papers  (2024-09-27T13:27:29Z)
• Cyber Knowledge Completion Using Large Language Models [1.4883782513177093]
  Integrating the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) has expanded their cyber-attack surface. Assessing the risks of CPSs is increasingly difficult due to incomplete and outdated cybersecurity knowledge. Recent advancements in Large Language Models (LLMs) present a unique opportunity to enhance cyber-attack knowledge completion.
  arXiv  Detail & Related papers  (2024-09-24T15:20:39Z)
• KGV: Integrating Large Language Models with Knowledge Graphs for Cyber Threat Intelligence Credibility Assessment [38.312774244521]
  We propose a knowledge graph-based verifier for Cyber Threat Intelligence (CTI) quality assessment framework. Our approach introduces Large Language Models (LLMs) to automatically extract OSCTI key claims to be verified. To fill the gap in the research field, we created and made public the first dataset for threat intelligence assessment from heterogeneous sources.
  arXiv  Detail & Related papers  (2024-08-15T11:32:46Z)
• CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization [14.287652216484863]
  We present CTISum, a new benchmark for CTI summarization task. Considering the importance of attack process, a novel fine-grained subtask of attack process summarization is proposed.
  arXiv  Detail & Related papers  (2024-08-13T02:25:16Z)
• Profile of Vulnerability Remediations in Dependencies Using Graph Analysis [40.35284812745255]
  This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model. We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-08T02:01:47Z)
• Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management [0.0]
  This paper proposes an innovative Attention-GAN framework for enhancing cybersecurity, focusing on anomaly detection. The proposed approach aims to generate diverse and realistic synthetic attack scenarios, thereby enriching the dataset and improving threat identification. Integrating attention mechanisms with Generative Adversarial Networks (GANs) is a key feature of the proposed method. The attention-GAN framework has emerged as a pioneering approach, setting a new benchmark for advanced cyber-defense strategies.
  arXiv  Detail & Related papers  (2024-02-25T01:10:55Z)

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.