Cybersecurity threats are rising in frequency and sophistication, which calls for the enhancement of better, real-time threat detection systems. This paper introduces a cyber threat intelligence (CTI) platform that combines a deep learning-based detection model with real-time log analysis with the utilization of security information and event management (SIEM) systems. A deep neural network trained with stochastic gradient descent (SGD) is at the heart of the proposed gadget, which uses log data to detect malicious activity. Logs are collected and consumed from the Wazuh platform, permitting real-time correlation and possibility tracking. A tailored dashboard presents a friendly interface for visualizing alerts and designs. The suggested structure mixes detection precision, machine scalability, and functioning responsiveness in changing network environments. Experimental outcomes demonstrate that the model is highly accurate and responsive in determining dangers, structuring its feasibility for real-time business environments.
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DOI: https://doi.org/10.63328/IJRDES-V7CIP14
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