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Cited by 36Network Security and Intrusion DetectionInformation and Cyber SecuritySpam and Phishing Detection

Threat Detection and Response Using AI and NLP in Cybersecurity

Open access
Ismail, Dr. Walaa Saber·2024·Journal of Internet Services and Information Security
DOI 10.58346/jisis.2024.i1.013
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Abstract

Introduction: In an age of rapid technical innovation and a growing digital world, protecting sensitive data from cyberattacks is crucial. The dynamic and complicated nature of these attacks requires novel cybersecurity solutions. Methods: This study analyses how Artificial Intelligence (AI) and Natural Language Processing (NLP) strengthen cybersecurity. The qualitative research approach is followed to gather data through a literature review of relevant scholarly articles and conduct interviews with cybersecurity specialists. Results: Recent AI advances have greatly enhanced the detection of anomalous patterns and behaviors in huge datasets, a key threat identification tool. NLP has also excelled at detecting malevolent intent in textual data, such as phishing efforts. AI and NLP enable adaptive security policies, enabling agile responses to evolving security issues. Expert interviews confirm that AI and NLP reduce false positives, improve threat intelligence, streamline network security setups, and improve compliance checks. These technologies enable responsive security policies, which give a strategic edge against developing security threats. AI and NLP's predictive skills could revolutionize cybersecurity by preventing threats. Conclusion: This study shows that AI and NLP have improved cybersecurity threat detection, automated incident response, and adaptive security policies. Overcoming threat detection, aggressive attacks and data privacy issues is essential to properly leveraging these advances and strengthening cyber resilience in a changing digital landscape.

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  • Deployment inside SIEM requires calibrated scores, adaptive thresholds, and real-time scoring paths so analysts can act on ranked anomalies rather than raw detector outputs.

    “Second, deployment inside SIEM requires calibrated scores, adaptive thresholds, and real-time scoring paths so analysts can act on ranked anomalies rather than raw detector outputs [2,11,12].”

    From A SIEM-Integrated Cybersecurity Prototype for Insider Threat Anomaly Detection Using Enterprise Logs and Behavioural Biometrics (2026) · cites this paper as [11]

    The “Safeguarding national security and preventing data” and “Triage and Response Automation” sections say AI-driven triage prioritizes security events so urgent risks receive faster responses. The copy does not specify SIEM deployment, calibrated scores, adaptive thresholds, or real-time scoring paths; its discussion of adaptive security policies is not a discussion of scoring thresholds. Page numbers were not provided. (LLM, conf=0.85)

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Research works printed but not linked: 6

  1. Abdel-Rahman, M. (2023). Advanced cybersecurity measures in IT service operations and their crucial role in safeguarding enterprise data in a connected world. Eigenpub Review of Science and Technology, 7(1), 138-158.
  2. Ahmad, A., Tariq, A., Hussain, H.K., & Gill, A.Y. (2023). Equity and Artificial Intelligence in Surgical Care: A Comprehensive Review of Current Challenges and Promising Solutions. BULLET: Multidisciplinary Journal of Science, 2(2), 443-455.
  3. Hassan, M., Aziz, L.A.R., & Andriansyah, Y. (2023). The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance. Reviews of Contemporary Business Analytics, 6(1), 110-132.
  4. Kumar, S., Gupta, U., Singh, A.K., & Singh, A.K. (2023). Artificial intelligence: revolutionizing cyber security in the digital era. Journal of Computers, Mechanical and Management, 2(3), 31-42.
  5. Mishra, A., & Yadav, P. (2020). Anomaly-based IDS to detect attack using various artificial intelligence & machine learning algorithms: a review. In 2 nd International Conference on Data, Engineering and Applications (IDEA), 1-7.
  6. Wong, S.K., & Yiu, S.M. (2020). Location spoofing attack detection with pre-installed sensors in mobile devices. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 11(4), 16-30.

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Authors
Ismail, D. W. S.
Venue
Journal of Internet Services and Information Security
Publisher
SASA Publications
Published
2024-03-02
Volume
14
Issue
1
Pages
195-205
ISSN
  • 2182-2069 (ISSN-L)
  • 2182-2077
DOI
10.58346/jisis.2024.i1.013
OpenAlex
W4392344890
Cited by
34
References
28
Citations by year
2026: 15 · 2025: 13 · 2024: 6
FWCI
5.68
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
Journal of Internet Services and Information Security (publishedVersion, cc-by-nc)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
cyber threat detection, natural language processing, artificial intelligence, phishing detection, automated incident response, adaptive security policies, threat intelligence, false positive reduction, network security, data privacy, cyber resilience

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