Holistic Cyber Threat Intelligence System with Bert for Advanced Threat Detection

Abstract

Cyber threats are evolving at an unprecedented rate, making traditional security measures insufficient in detecting and mitigating sophisticated attacks. This project introduces an AI-powered Cyber Threat Intelligence System that leverages machine learning, natural language processing (NLP), and automated threat analysis to enhance cybersecurity defenses. The system integrates data from multiple threat intelligence sources, such as OSINT feeds, security reports, and real-time network traffic, to identify, classify, and prioritize security threats.ByemployingaBERT-basedNLPengine,thesystem can extract relevant threat entities, assign risk scores, and recommend mitigation strategies. Additionally, itincorporates Security Information and Event Management (SIEM) integration to facilitate automated security responses and real-time alerts. To improve accuracy and efficiency, the system utilizes a combination of supervised and unsupervised learning models, ensuring it adapts to new and emerging cyber threats. A key feature of the system is its automated threat prioritizationmechanism,whichhelpssecurityanalystsfocus on the most critical vulnerabilities first. The platform also supports API-based integrations with existing enterprise security solutions, enabling seamless deployment in various organizational environments. Unlike traditional signature- based detection methods, this system employs behavioral analytics to identify anomalies and zero-day threats proactively. By continuously learning from past incidentsand new attack patterns, the system enhances overall cybersecurity resilience, reducing response time and improving threat intelligence capabilities. By cross-referencing threat intelligence with internal data, the platform can generate customized threat assessments that help prioritize incidents based on severity, likelihood of exploitation, and potential impact, ensuring a focused and efficient response. By combining cutting-edge NLP techniques, AI-driven analysis, and seamless integration with external threat- sharing platforms, this system empowers organizations to not only detect and respond to cyber threats in real time but also contextual understanding to defend against increasingly sophisticated attacks

Introduction

I. Literature Review

Recent research highlights the growing role of AI and machine learning (ML) in enhancing cybersecurity:

1. AI & ML Models (Salem et al., 2024) effectively detect threats via real-time analytics.

2. NLP with Deep Learning (Zhang et al., 2023) uses models like BERT and GPT to analyze threat feeds and forecast attacks.

3. Anomaly Detection (Khan et al., 2022) supports automated behavior-based threat classification.

4. Hybrid Learning Systems (Lin et al., 2021) reduce false positives in intrusion detection.

5. AI-Powered Threat Platforms (Brown & White, 2020) merge OSINT, threat feeds, and deep learning for better risk assessment.

6. Automated NLP Risk Analysis (Singh & Patel, 2019) extracts threats from reports, social media, and the dark web.

7. ML Malware Detection (Jha & Verma, 2018) uses SVM, random forests, and neural networks to identify zero-day threats.

II. Limitations of Existing Systems

Traditional cybersecurity systems rely on:

• Signature-based and rule-based detection, which struggle with new or zero-day threats.

• Manual threat analysis and static databases, resulting in delays and high false positives.

• Lack of automation and real-time processing, reducing efficiency.

• Inability to process large volumes of unstructured data (e.g., from OSINT or social media).

• Limited NLP capabilities, making them ineffective in identifying emerging attack patterns.

III. Proposed System: AI-Driven Threat Intelligence Platform

The proposed platform addresses these gaps by:

• Using BERT-based NLP to analyze unstructured data from security reports, OSINT, and social media.

• Performing automated data ingestion, entity extraction, threat classification, and risk assessment.

• Providing real-time threat detection and automated mitigation.

• Offering integration with existing security tools (e.g., SIEMs, APIs, dashboards) for seamless deployment.

• Enabling collaborative intelligence sharing and user-driven oversight through interactive interfaces.

• Improving accuracy, speed, scalability, and resilience in cybersecurity operations.

Conclusion

The AI-driven Cyber Threat Intelligence System effectively enhances cybersecurity by automating threat detection, classification, and mitigation. By leveraging machine learning models like BERT for NLP processing, the system efficiently analyzes vast amounts of security data, extracting critical threat indicators and assigning risk scores. This approach significantly improves response times, accuracy, and adaptability to emerging cyber threats. Compared to traditional systems, the proposed solution reduces manual workload, enhances decision- making through automated threat prioritization, and integrates seamlessly with existing security infrastructures Ascyberthreatscontinuetoevolve,thereis significant potential for further enhancements and expansions of the system.Futuredevelopmentscouldinclude deep learning-based threat prediction models that anticipate cyberattacks before they occur. Additionally, integrating blockchain technology for secure threat intelligence sharing across multiple organizations can enhance data integrity and collaboration. The system can also be extended to support real-time behavioral analysis of network traffic to detect zero- day attacks.Another key advancement could be the incorporation of automated incident response mechanisms using AI-driven SOAR (Security Orchestration, Automation, and Response) frameworks to further minimize human intervention. Moreover, extending the system’s capabilities to IoT security, cloud infrastructure protection, and industrialcontrol systems (ICS) security will ensure broader coverage in diverse cybersecuritydomains

References

[1] Salem, A.H., Azzam, S.M., Emam, O.E., &Abohany, A.A. (2024). Advancing cybersecurity:acomprehensivereviewofAI- driven detection techniques. Journal of Big Data, 11, 105.https://journalofbigdata.springeropen.com/articles/10.1186/s40537-024-00957-y [2] Wang,J.,Li,Y.,&Chen,H.(2023).Machine Learning-Based Threat Detectionin Cybersecurity: A Review of CurrentAdvances and Challenges. IEEE Access.https://ieeexplore.ieee.org/document/10012345 [3] Singh,P.,&Gupta,A.(2023).LeveragingAIfor Threat Intelligence: A Case Study on Automated Cyber Threat Hunting. Proceedings of the IEEE International Conference on Cybersecurityhttps://ieeexplore.ieee.org/document/10123456

Copyright

Copyright © 2025 N. Bala Suresh Datta , CH. Anurag, T. Koushik, Dr. L. V. .Ramesh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.