A comprehensive ATM security framework for detecting abnormal human activity via granger causality-inspired graph neural network optimized with eagle-strategy supply-demand optimization

Abstract

The daily increase of criminal activity has made real-time human activity detection crucial for the protection & surveillance of public spaces, including bank-automated teller machines (ATM). To overcome the difficulty of online identification of anomalous activity in bank automated teller machines. A Comprehensive ATM Security Framework for Detecting Abnormal Human Activity via Granger Causality-Inspired Graph Neural Network optimized with Eagle-Strategy Supply-Dem& Optimization (ATM-DAHA-GCIGNN-ESSDO) is proposed in this manuscript. Initially, the input videos are gathered from DCSASS Dataset & UCF Crime Dataset. Then, the video is pre-processed by using Reverse Lognormal Kalman Filtering (RLKF) for cleaning noisy data. Granger Causality-Inspired Graph Neural Network (GCIGNN) is employed for detect abnormal human activities in ATM machine. Abuse, Arrest, Arson, Assault, Burglary, Explosion, Fighting, Road Accidents, Robbery, Shooting, Shoplifting, Stealing, V&alism for DCSASS Dataset & Abuse, Arrest, Assault, Arson, Burglary, Explosion, Fighting, Normal Videos, Road Accidents, Shoplifting, Shooting, Robbery, Stealing, V&alism for UCF Crime Dataset. The Eagle-Strategy Supply-Dem& Optimization (ESSDO) is implemented to enhance the parameters of GCIGNN. The proposed method is implemented & the efficiency is estimated using some performance metrics, like Accuracy, Recall, F1-score, precision, False Discovery Rate & Computational time. The performance of the ATM-DAHA-GCIGNN-ESSDO approach attains 24.39%, 35.71%, & 25.55% higher Accuracy; 22.15%, 24.21%, & 43.52% higher Recall. The proposed ATM-DAHA-GCIGNN-ESSDO framework outperforms the existing approaches for identifying aberrant human activity in ATM & criminal situations. Finally, the proposed approach demonstrates its potential as a reliable solution for real-time security & surveillance applications.