Foundations of Deep Neural Networks for Predictive Analytics in Critical Infrastructure Security
S Saravanan, D Shobana, S. Prayla Shyry · 2025
The integration of multimodal data has emerged as a pivotal approach to bolstering critical infrastructure security, enabling enhanced situational awareness and predictive analytics. This chapter delves into the foundational principles, challenges, and methodologies underpinning multimodal data integration, with a particular emphasis on the fusion of heterogeneous data sources such as sensor readings, video surveillance, and network logs. Key topics include advanced preprocessing techniques, optimal representation learning, and the application of deep neural networks for real-time threat detection and response. Special focus was given to the role of distributed processing frameworks, lightweight edge-based models, and attention mechanisms in overcoming the challenges of data heterogeneity, scalability, and latency. By addressing these dimensions, this work underscores the transformative potential of multimodal analytics in fortifying infrastructure against evolving cyber-physical threats. The insights presented pave the way for adaptive, resilient, and intelligent security systems capable of safeguarding critical assets in an increasingly interconnected world.