Deep Learning for Cyber Threat Intelligence: Enhancing Security Through Automated Data Analysis
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Abstract
Cyber threat intelligence (CTI) is vital for understanding and mitigating risks in the digital landscape. This paper explores the use of deep learning techniques to automate the analysis of threat intelligence data, including malware samples, threat actor profiles, and attack vectors. By employing convolutional neural networks and natural language processing, the model extracts meaningful patterns and insights from unstructured data sources, improving the speed and accuracy of threat identification. Case studies demonstrate the model's effectiveness in predicting emerging threats and informing proactive security measures, thereby enhancing overall cybersecurity resilience.
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