Predictive AI Model for Identifying Emergency Cyber Security Threads

Authors: Sarita Jadhav, Sejal Bargat, Arjun Kadam, Rahul Bhadane

Country: India

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Abstract: The rapid escalation of cyber-attacks in modern digital ecosystems has increased the need for intelligent and proactive security mechanisms capable of identifying threats before they escalate into critical incidents. Predictive AI models leverage advanced machine learning, deep learning, and real-time data analytics to forecast potential cyber security breaches by analyzing patterns, anomalies, and behavioral deviations across network traffic and system logs. This paper presents a comprehensive study on the design and implementation of predictive AI frameworks that enable early detection of emergency cyber security threats, including malware intrusions, zero-day exploits, phishing activities, and distributed denial-of-service (DDoS) attacks. The proposed approach integrates supervised and unsupervised learning algorithms such as anomaly detection models, neural networks, and ensemble classifiers to enhance detection accuracy and reduce false-positive rates. Experimental evaluations demonstrate improved responsiveness and adaptability when compared to traditional rule-based systems, enabling organizations to mitigate risks proactively. The findings indicate that predictive AI not only strengthens real-time threat identification but also supports automated decision-making, resource prioritization, and incident response planning, thereby contributing to more robust and resilient cyber security infrastructures

Keywords: Cyber Attack, cyber security, DDoS.

Paper Id: 232994

Published On: 2026-03-09

Published In: Volume 14, Issue 2, March-April 2026

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