• Parul Kashyap
• Neha Yadav

Traditional defense techniques, which mostly rely on static rules and signature-based detection, face considerable hurdles due to the complexity of dynamic domain cyber threats, which have expanded dramatically with the rapid expansion of the digital landscape. Given these new issues, this paper offers a thorough analysis of how artificial intelligence (AI), and in particular machine learning (ML), may strengthen cybersecurity systems by using sophisticated and flexible threat detection techniques. In order to detect abnormalities, anticipate possible cyberattacks, and continuously learn from new patterns of hostile behavior in real time, the study focuses on the design, development, and practical use of machine learning algorithms. Several learning paradigms, such as supervised, unsupervised, and reinforcement learning models, are thoroughly compared in order to assess each one's and all of them's suitability for use in contemporary cybersecurity applications, particularly with regard to request and intrusion detection. To guarantee robustness, scalability, and generalizability, the models undergo rigorous training and testing on both simulated and real-world cybersecurity datasets. When compared to traditional rule-based protection systems, experimental results show a notable improvement in detection accuracy, a decrease in response time, and increased resilience against zero-day attacks. This study highlights the need for ongoing innovation and adaptability in cybersecurity procedures in addition to showcasing the useful potential of AI-driven systems in bolstering cyber defenses. Additionally, by highlighting developments in machine learning, pattern recognition, and intelligent automation, it creates new opportunities for future research into the integration of AI technologies, which could collectively reshape the way cybersecurity frameworks are conceived and applied in the years to come.

Threat detection, preemptive security, intrusion prevention, AI-based security systems, cyber threat mitigation, machine learning, artificial intelligence, cybersecurity, predictive analytics, and intelligent security solutions.

"Improving Cybersecurity Systems with Artificial Intelligence: A Model Based on Machine Learning for Preemptive Threat Identification and Mitigation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 10, page no.c297-c304, October-2025, Available :http://www.jetir.org/papers/JETIR2510243.pdf

"Improving Cybersecurity Systems with Artificial Intelligence: A Model Based on Machine Learning for Preemptive Threat Identification and Mitigation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 10, page no. ppc297-c304, October-2025, Available at : http://www.jetir.org/papers/JETIR2510243.pdf