TITLE:
Design of an Intelligent Campus Surveillance System, Based on Cognitive Security and the Internet of Things, for the Detection of Suspicious Activities
AUTHORS:
Chely Ongondza, Vivien Armel Eyangolo, Katalay Pierre Kafunda
KEYWORDS:
Cognitive Security, Internet of Things (IoT), Suspicious Activity Detection, Computer Vision, Machine Learning
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.3,
March
17,
2026
ABSTRACT: University campus security has become a major issue with the increasing digitization and interconnection of infrastructure. This article proposes the design of an intelligent surveillance system based on a cognitive security approach integrating the Internet of Things (IoT). The model combines computer vision, behavioral analysis, and machine learning to detect suspicious movements and abnormal behavior in real time. IoT sensors collect data from multiple sources (videos, motion detectors, access control devices), while the cognitive layer provides contextual interpretation and adaptive decision-making. The proposed architecture aims to improve responsiveness, detection accuracy, and resilience against physical and cyber threats, while respecting the confidentiality and reliability constraints inherent in smart environments.