Real-Time Deep Learning Engines for Enterprise-Scale Intrusion Detection and Cyber Risk Management

Abstract: The digital transformation of enterprises is occurring so quickly that attack surfaces are now much larger, and
intrusion detection and cyber risk management in real time are becoming more complex. Traditional signature-based and ruledriven security systems are unable to handle high-volume, high-velocity network traffic and evolving attack patterns, leading
to delayed attack detection and greater breach impact. The goal of this research is to implement and test the performance of
real-time deep learning engines for detecting sophisticated intrusions while enabling enterprise-level cyber risk management.
The current framework proposes combining deep neural architectures (i.e., convolutional neural networks to learn spatial
features and recurrent models to learn temporal dependencies) within the streaming analytics pipeline. The system is distributed
to provide low-latency inference for continuous network flows. Experimental evaluation on large-scale benchmark and
enterprise-mimicking datasets shows that the proposed engine achieves 98.6% detection accuracy, reduces false-positive rates
by 32% compared to traditional IDS solutions, and maintains an average detection latency of less than 120 ms under peak loads.
These results indicate that real-time deep learning engines can significantly improve enterprise intrusion detection capabilities,
achieving both high accuracy and operational scalability. The conclusion of the given study is that deep learning-driven, realtime IDS frameworks are a viable foundation for next-generation cyber risk management systems in enterprise environments.