Approaches of Deep Learning for Critical Security Representation in the Society
Ratnesh Kumar Shukla, Arvind Kumar Tiwari, Manmohan Mishra, Ashok Tripathi, Shivam Bhardwaj · Advances in information security, privacy, and ethics book series · 2025
Modern society depends on key infrastructures being resilient, although a large portion of deployed infrastructure has not yet completely benefited from contemporary technological advancements. Deep learning and critical infrastructure protection are two distinct domains that are intersected in this chapter. Deep learning can increase resilience in a variety of industries, including finance, electronics, education, and security. Computer systems that automatically recognize patterns from example data and image/video analysis are the subject of deep learning research. Additionally, this involves automatically segmenting and identifying the corrected objects of interest from text, audio, video, and image data. When it comes to protecting vital infrastructures, this technology may be able to automate threat assessments. The latest developments in deep learning for computer vision systems are covered in this chapter, and the ideas are used to make critical infrastructures more resilient.