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The Industrial Internet of Things is becoming the novel driving force in the automotive industry, assembly travel more suitable for individuals. Despite this, there are still a number of obstacles to overcome, such as detecting illegal drivers, identifying legitimate drivers, and evaluating driving behavior. The use of deep learning networks has been attempted by many academics to address the issues of detecting illicit drivers and identifying legal drivers, however the gathering and analysis of data on driving behavior are still subject to several restrictions. Furthermore, insufficient focus has been placed on examining a driver's behavior. To deal with the aforementioned concerns, we carried out a thorough investigation on driving behavior patterns and constructed an Multi-Task Learning (MTL) based network. In the first place, we gather real-world data from an automobile and analyze it for traits related to driving. After that, a novel MTL network is built utilizing a Long Short Term Memory network to detect unlawful drivers, identify legal drivers, and evaluate driving behavior. To strengthen their argument, the authors could conduct experiments or case studies comparing the performance and efficiency of MTL with single-task learning or other methods. By quantifying these factors, they could provide more concrete evidence to support their claim that MTL offers time and cost savings. [ABSTRACT FROM AUTHOR]