AI-Powered Cyber Defense: Adaptive Algorithms for Proactive Threat Mitigation
Keywords:
Artificial Intelligence, Cybersecurity, Adaptive Algorithms, Threat MitigationAbstract
The relentless evolution of cyber threats, characterized by increasing sophistication and dynamism, poses an existential challenge to digital security. Traditional, signature-based cybersecurity solutions are often reactive and insufficient against novel or polymorphic attacks. This article investigates the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing cybersecurity, specifically focusing on the development and application of adaptive algorithms for proactive threat mitigation. We explore methodologies encompassing intelligent threat detection, predictive risk assessment, and automated response mechanisms. Findings indicate that AI/ML significantly enhances the accuracy and speed of threat identification, enables continuous learning from new attack patterns, and reduces human workload. While challenges such as data quality, model interpretability, and the emergence of adversarial AI persist, the strategic integration of AI/ML is essential for building resilient, future-proof digital defense strategies capable of combating the constantly shifting threat landscape.
References
B. R. Maddireddy and B. R. Maddireddy, “Evolutionary algorithms in ai-driven cybersecurity solutions for adaptive threat mitigation,” International Journal of Advanced Engineering Technologies and Innovations, vol. 1, no. 2, pp. 17–43, 2021.
A. Mumtaz and H. Liu, “Evolutionary algorithms and ai in cybersecurity: Adaptive threat mitigation strategies using big data and iot,” 2021.
S. Rangaraju, “Ai sentry: Reinventing cybersecurity through intelligent threat detection,” EPH-International Journal of Science And Engineering, vol. 9, no. 3, pp. 30–35, 2023.
O. U. Khan, S. M. Abdullah, A. O. Olajide, A. I. Sani, S. M. W. Faisal, A. A. Ogunola, and M. D. Lee, “The future of cybersecurity: Leveraging artificial intelligence to combat evolving threats and enhance digital defense strategies,” Journal of Computational Analysis and Applications, vol. 33, no. 8, 2024.
A. R. P. Reddy, “The role of artificial intelligence in proactive cyber threat detection in cloud environments,” NeuroQuantology, vol. 19, no. 12, pp. 764–773, 2021.
M. Rizvi, “Enhancing cybersecurity: The power of artificial intelligence in threat detection and prevention,” International Journal of Advanced Engineering Research and Science, vol. 10, no. 5, pp. 055–060, 2023.
H. Raza, “Proactive cyber defense with ai: Enhancing risk assessment and threat detection in cybersecurity ecosystems,” Journal Name Missing, 2021.
A. Manoharan and M. Sarker, “Revolutionizing cybersecurity: Unleashing the power of artificial intelligence and machine learning for next-generation threat detection,” DOI: https://www.doi.org/10.56726/IRJMETS32644, vol. 1, 2023.
Y. Weng and J. Wu, “Leveraging artificial intelligence to enhance data security and combat cyber attacks,” Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, vol. 5, no. 1, pp. 392–399, 2024.
R. H. Chowdhury, N. U. Prince, S. M. Abdullah, and L. Mim, “The role of predictive analytics in cybersecurity: Detecting and preventing threats,” World Journal of Advanced Research and Reviews, vol. 23, no. 2, pp. 1615–1623, 2024.
J. Jones, E. Harris, Y. Febriansah, A. Adiwijaya, and I. N. Hikam, “Ai for sustainable development: Applications in natural resource management, agriculture, and waste management,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 143–149, 2024.
A. Bécue, I. Praça, and J. Gama, “Artificial intelligence, cyber-threats and industry 4.0: Challenges and opportunities,” Artificial Intelligence Review, vol. 54, no. 5, pp. 3849–3886, 2021.
A. Nassar and M. Kamal, “Machine learning and big data analytics for cybersecurity threat detection: A holistic review of techniques and case studies,” Journal of Artificial Intelligence and Machine Learning in Management, vol. 5, no. 1, pp. 51–63, 2021.
N. G. Camacho, “The role of ai in cybersecurity: Addressing threats in the digital age,” Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, vol. 3, no. 1, pp. 143–154, 2024.
A. N. Raji, A. O. Olawore, A. Ayodeji, and J. Joseph, “Integrating artificial intelligence, machine learning, and data analytics in cybersecurity: A holistic approach to advanced threat detection and response,” 2023.
R. Gupta and P. Srivastava, “Artificial intelligence and machine learning in cyber security applications,” in Cyber Security Solutions for Protecting and Building the Future Smart Grid. Elsevier, 2025, pp. 271–296.
O. C. Obi, O. V. Akagha, S. O. Dawodu, A. C. Anyanwu, S. Onwusinkwue, and I. A. I. Ahmad, “Comprehensive review on cybersecurity: modern threats and advanced defense strategies,” Computer Science & IT Research Journal, vol. 5, no. 2, pp. 293–310, 2024.
F. Ekundayo, I. Atoyebi, A. Soyele, and E. Ogunwobi, “Predictive analytics for cyber threat intelligence in fintech using big data and machine learning,” Int J Res Publ Rev, vol. 5, no. 11, pp. 1–15, 2024.
U. Raharja, Y. P. Sanjaya, T. Ramadhan, E. A. Nabila, and A. Z. Nasution, “Revolutionizing tourism in smart cities: Harnessing the power of cloud-based iot applications,” CORISINTA, vol. 1, no. 1, pp. 41–52, 2024.
S. Malik, P. K. Malik, and A. Naim, “Opportunities and challenges in new generation cyber security applications using artificial intelligence, machine learning and block chain,” Next-Generation Cybersecurity: AI, ML, and Blockchain, pp. 23–37, 2024.
D. Manongga, U. Rahardja, I. Sembiring, Q. Aini, and A. Wahab, “Improving the air quality monitoring framework using artificial intelligence for environmentally conscious development,” HighTech and Innovation Journal, vol. 5, no. 3, pp. 794–813, 2024.
P. Bibi, “Artificial intelligence in cybersecurity: Revolutionizing database management for enhanced protection,” 2022.
M. Ozkan-Okay, E. Akin, O. Aslan, S. Kosunalp, T. Iliev, I. Stoyanov, and I. Beloev, “A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions,” IEEe Access, vol. 12, pp. 12 229–12 256, 2024.
B. P. Sharma, “Evaluating the role of artificial intelligence in enhancing cyber threat detection and response mechanisms,” Journal of Digital Transformation, Cyber Resilience, and Infrastructure Security, vol. 8, no. 12, pp. 1–10, 2024.
M. Brundage, S. Avin, J. Clark, H. Toner, P. Eckersley, B. Garfinkel, A. Dafoe, P. Scharre, T. Zeitzoff, B. Filar et al., “The malicious use of artificial intelligence: Forecasting, prevention, and mitigation,” arXiv preprint arXiv:1802.07228, 2018.
E. Guustaaf, U. Rahardja, Q. Aini, H. W. Maharani, and N. A. Santoso, “Blockchain-based education project,” Aptisi Transactions on Management, vol. 5, no. 1, pp. 46–61, 2021.
D. Arora, P. Tyagi, P. Dadhich et al., “Exploring the impact of artificial intelligence on cyber security: Challenges, opportunities, and future trends,” 2024.
F. Tao, M. S. Akhtar, and Z. Jiayuan, “The future of artificial intelligence in cybersecurity: A comprehensive survey,” EAI Endorsed Transactions on Creative Technologies, vol. 8, no. 28, pp. e3–e3, 2021.
M. Malatji and A. Tolah, “Artificial intelligence (ai) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive ai,” AI and Ethics, pp. 1–28, 2024.
H. Hussain, M. Kainat, T. Ali et al., “Leveraging ai and machine learning to detect and prevent cyber security threats,” Dialogue Social Science Review (DSSR), vol. 3, no. 1, pp. 881–895, 2025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Dr. Kate Mcallister, Dr. Sergey Volkov
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain full copyright of their published work. By submitting to an ERDAST journal and upon acceptance of the article, the author(s) agree to grant the journal a non-exclusive license to publish, reproduce, distribute, and archive the article. All articles are published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Under this license: Others may copy, distribute, display, remix, adapt, and build upon the work, even commercially, As long as proper credit is given to the original author(s) and source, A link to the license is provided, an, Any changes are clearly indicated.
???? License link: https://creativecommons.org/licenses/by/4.0/
