Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response

Crossmark

Article Sidebar

Page Numbers: 268-286
Download PDF
Published: Oct 5, 2024
Digital Object Identifier: 10.58578/ijemt.v2i3.3904
Save this to:
Save to Mendeley Download RIS

Article Metrics:
Viewed: 641 times
Downloaded: 291 times
Article can trace at:

Author Fee:
Free Publication Fees for Foreign Researchers (USD 0.00)
Check for article on SINTA:

LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers

Connected Papers:
Connected Papers


Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.


Authors:
Chinenye Cordelia Nnamani1
Copyright :

Authors retain copyright and grant the journal right of first publication.

Explore Our Journals
Find the journal that best fits your research. If this journal does not match your manuscript’s scope, explore our portfolio below to select a more suitable publication venue.

Main Article Content

Chinenye Cordelia Nnamani
Institute of Management and Technology, Enugu, Nigeria

Abstract

This Article thoroughly examines the revolutionary impact of Artificial Intelligence (AI) on improving threat detection and response tactics within the swiftly changing realm of cybersecurity. Conventional security measures, struggling against the complexity of contemporary cyber threats, fail to provide adequate protection. In response, AI, driven by machine learning algorithms and predictive analytics, becomes a dynamic and adaptive entity strengthening digital defenses.  The investigation commences with a comprehensive analysis of the methods by which AI enhances danger detection. Behavioral analytics utilizes AI to assess user behaviors and network activity, creating a proactive baseline, while anomaly detection and predictive analysis harness machine learning to recognize deviations from the norm and forecast potential dangers. This comprehensive strategy enables organizations to remain proactive against emerging cyber threats.  Moreover, the study explores the crucial function of AI in incident response. AI-driven automated incident analysis expedites reaction times by rapidly analyzing and prioritizing security warnings. The amalgamation of AI with threat intelligence streams guarantees a perpetually updated knowledge repository, enabling organizations to respond adeptly to emerging dangers. The dynamic flexibility of AI allows systems to evolve and learn from each incidence, hence enhancing their defensive capacities over time.  The discourse recognizes the significant advantages of AI in cybersecurity while simultaneously addressing the obstacles associated with its application. False positives, a potential drawback, require a measured approach to prevent the perception of typical action as harmful. Ethical factors, including privacy concerns and responsible AI practices, highlight the necessity for a judicious and principled incorporation of AI in cybersecurity.  The paper underscores the essential collaborative synergy between human expertise and AI technologies. The essay emphasizes the importance of continuous investment in AI training programs for cybersecurity professionals, acknowledging that AI is best successful when enhanced by human insights. Additionally, it advocates for routine security audits to assess and refine cybersecurity protocols, collaborative research efforts to tackle ethical issues, and user education activities to strengthen collective defenses. As the digital landscape evolves, the incorporation of AI in cybersecurity seems not as a cure-all but as a formidable ally. This partnership guarantees a robust protection against increasingly complex cyber-attacks, reinforcing the basis for a secure digital future.

Keywords:
Share Article:

Citation Metrics:

Scopus

Downloads

Download data is not yet available.

Citation Metrics & Similar Scopus Articles

Data source Crossref
0
citations
Citation counts are source-specific and may differ because database coverage, reference matching, and update schedules are different. Counts are not added together. Crossref values represent citation links registered and matched by Crossref.
Check Secondary Documents in Scopus
Open this article in Scopus, then check the Secondary documents tab. Use Manual Citation Fallback only for counts you have verified manually.
Open in Scopus
Similar Scopus Articles
Scopus
  1. Li R. (2027)
    IrMn-Cluster-Based Artificial Metalloenzymes with Radiosensitized Systemic Antitumor Responses to Prevent Malignant Tumor Metastasis and Recurrence
    Nano Micro Letters, 19(1)
  2. Aimenov Z.T. (2027)
    Artificial graphite from Shubarkol coal obtained by sublimation of carbon atoms into the gas phase followed by desublimation into high-purity graphite
    Kompleksnoe Ispolzovanie Mineralnogo Syra, 341(2), 27-35
  3. Edwin E. (2027)
    Inflammation-driven multiorgan Dysfunction: Biomarker-Guided diagnosis, mechanistic insights and emerging precision therapeutic strategies
    Advances in Biomarker Sciences and Technology, 9(1), 1-25

Article Details

How to Cite
Nnamani, C. C. (2024). Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response. International Journal of Education, Management, and Technology, 2(3), 268-286. https://doi.org/10.58578/ijemt.v2i3.3904

Creative Commons License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

References

Bace, R. G., & Mell, P. (2001). NIST Special Publication 800-61: Computer Security Incident Handling Guide. National Institute of Standards and Technology.
Bishop, M. (2003). Introduction to Computer Security. Addison-Wesley.
Chuvakin, A., & Schmidt, K. (2015). Security and Log Management: A Practical Guide. Elsevier.
Das, A., & Sharma, R. (2020). Artificial Intelligence in Cybersecurity: Threats and Challenges. Journal of Cybersecurity Technology, 4(1), 34-50.
Dhanjani, N., & Ransbotham, S. (2019). Machine Learning in Cybersecurity: A Survey. IEEE Access, 7, 205066-205078.
Fiser, A., & Manas, J. (2018). AI-Driven Threat Detection: The Future of Cybersecurity. Cybersecurity Journal, 4(3), 245-256.
Ganaie, M. A., & Makhdoom, I. (2021). AI-Based Cybersecurity Solutions: A Review. International Journal of Information Security, 20(3), 175-187.
Garvey, A. (2020). The Role of AI in Cybersecurity. TechTarget.
Huang, L., & Yang, L. (2021). A Review on Machine Learning for Cybersecurity. IEEE Transactions on Emerging Topics in Computing, 9(2), 859-875.
Kaur, M., & Dhir, A. (2021). Artificial Intelligence in Cybersecurity: Opportunities and Challenges. International Journal of Cybersecurity and Cyber-Physical Systems, 2(1), 22-32.
Khan, A., & Babar, M. (2020). Integrating AI and Cybersecurity: Current Trends and Future Directions. Journal of Information Security and Applications, 55, 102-113.
Kim, D. H., & Kim, H. J. (2019). Real-time Cyber Threat Detection Using Machine Learning Algorithms. International Journal of Information Security, 18(5), 555-564.
Kolosnjaji, B., et al. (2018). Deep Learning for Cybersecurity: A Review. Computers & Security, 78, 168-182.
Lee, J., & Kim, S. (2020). Adversarial Machine Learning in Cybersecurity: A Survey. Journal of Computer Virology and Hacking Techniques, 16(2), 123-138.
Liu, X., et al. (2021). A Survey on AI-Based Cybersecurity Techniques. IEEE Communications Surveys & Tutorials, 23(3), 1753-1774.
Mahdavi, M., & Douran, N. (2020). The Future of AI in Cyber Defense. Journal of Cybersecurity Research, 5(2), 41-50.
Manogaran, G., & Srinivasan, K. (2018). Cybersecurity in the Age of AI. Future Generation Computer Systems, 82, 25-34.
Nunez, M., & Gonzales, J. (2020). The Ethics of AI in Cybersecurity. AI & Society, 35(1), 159-169.
Pal, A., & Dutta, A. (2020). AI Techniques for Cybersecurity. International Journal of Cyber Criminology, 14(1), 123-140.
Peeters, A., et al. (2019). Machine Learning Techniques for Cybersecurity: A Review. Computer Science Review, 33, 100-112.
Ranjan, R., et al. (2020). Cyber Threat Intelligence Using AI: A Review. Journal of Information Security and Applications, 53, 102-112.
Raghavan, V., & Rao, S. (2020). AI in Cybersecurity: An Overview. Cybersecurity and Privacy, 3(1), 45-56.
Salami, A., & Sadat, S. (2021). Understanding AI-Based Cybersecurity Solutions. Journal of Cybersecurity and Privacy, 1(1), 10-20.
Shafique, M., et al. (2020). AI-Powered Cybersecurity Frameworks. Journal of Cyber Security Technology, 4(2), 100-120.
Sweeney, L., & Zikopoulos, P. (2019). Big Data for Dummies. Wiley.
Tan, J., & Li, X. (2021). Behavioral Analytics for Cyber Threat Detection. IEEE Transactions on Information Forensics and Security, 16, 456-469.
Tharwat, A., et al. (2020). A Comprehensive Review of Machine Learning Techniques in Cybersecurity. Journal of Computer Virology and Hacking Techniques, 16(2), 1-24.
Törngren, M., & Gadd, M. (2020). AI for Cyber Defense: A Systematic Review. Journal of Information Technology, 35(2), 123-135.
Wentz, J. (2020). AI in Cybersecurity: Opportunities and Challenges. The Cyber Defense Review, 5(2), 89-103.
Zhang, Y., et al. (2021). Exploring AI Techniques for Cybersecurity Applications. Journal of Information Security and Applications, 57, 102-113.

Most read articles by the same author(s)