IoT-Inspired Intelligent Analysis Framework for Security Personnel

National security is one of the premier sectors of research and development in every country. Moreover, government organizations spend large funds to ensure technical advancements in the defense sector. However, major flaws in the routine activities of security personnel have resulted in severe tragedies. Conspicuously, a comprehensive Internet of Things (IoT)-based methodology has been presented to evaluate the integrity of an officer based on professional and personal activities. Specifically, the current study proposes a digital twin-inspired method for evaluating the overall performance of intelligence agency officers (IAOs). The probabilistic digital integrity estimate (DIE) is formalized through data analysis using the Bayesian belief model (BBM). Additionally, the multiscaled long term memory (MLSTM) model has been proposed to predict the overall integral behavior of IAO. The proposed system has been verified over a real-world data set with 42892 instances. Results show that the proposed technique surpasses comparative models in key metrics, such as temporal delay effectiveness (127.79 s), categorization analysis [precision (96.67%), specificity (97.08%), and sensitivity (97.55%)], decision-modeling efficacy [specificity (93.49%), precision (93.49%), and sensitivity (93.69%)], reliability (94.86%), and stability (82.0%).