A Literature Survey on AI-Based Secure Border Intrusion Detection Systems Using Deep Learning, IoT, and Encrypted Communication

Abstract: Border intrusion detection is a highpriority national security concern, as unauthorized crossings can facilitate smuggling, trafficking, and terrorism. Conventional surveillance systems relying on continuous human monitoring are limited by operator fatigue, delayed response, and degraded performance under poor environmental conditions such as fog, rain, and low light. This paper presents a literature survey of five recent studies that address AIdriven solutions for border and perimeter security, covering deep learning-based object tracking, hybrid encryption for secure image transmission, integrated surveillance pipelines, machine learning for wireless sensor network protection, and drone-based real-time monitoring. The survey critically analyses the advantages and limitations of each approach and proposes a conceptual integrated framework that combines multi-modal sensing, lightweight deep learning models, and encrypted communication for more reliable and efficient border surveillance.

Keywords: Border security, deep learning, YOLO, DeepSORT, object detection, encryption, IoT, wireless sensor networks, drone surveillance, intrusion detection.