Abstract
Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model.