# Lightweight Hybrid Deep Learning Architecture and Model for Security in IOT
## 1 Introduction  
The combination of Industrial Internet of Things applications and the manufacturing field acts as a big opportunity to enhance the base of the customer. Production-oriented applications, generally, improve the manufacturing process and reduce implementation complexity. They also enhance organizational communication and collaboration inside the company [1]. Likewise, the deployment of automated wireless communication technologies can ensure effective communication at any time and location, facilitating an industrial firm's successful growth and development. The importance of those networks based on the Internet of Things (IoT) in Industrial IoT applications is discussed in this section. Industrial organizations, on the other hand, continue to use conventional methods for continually improving industrial efficiency in tandem with adaptation in supervision, management, and control. This drives a tendency to look into new perspectives in industrial processes, as well as intellectual disciplines, to better understand the difficulties ahead [2]. As a result, the agile manufacturing applications, which allow information interchange, are in conflict with various businesses. There may also be challenges in bringing Industry 4.0 applications to industries. The examination of the elements that cause these issues allows businesses to better govern the transformation process. It also makes the transition easier. There are several solutions to analyze the criteria available in the literature. For this type of investigation, the decision-making procedures based on multiple criteria are useful [3]. The research related to Industrial IoT, also known as Industry 4.0, is generally undertaken in the field of manufacturing in developed countries, as the following conception was conceivedin the world's leading industrial economies [1]. The goal of this study is to assess the application of advanced digital technologies in Industrial Domains and in transition nations in the context of Industrial IoT, where a lot of attacks occur on the Industrial networks where security plays a crucial role. This assessment contains the security approach in comparison with the sophisticated digital technologies using a set of criteria. Meanwhile, the industrial sector has a gradual increase in the number of edge and control devices where various security and privacy issues have surfaced, posing a significant threat to the IIoT's security and credibility [4]. Intruders may be able to take advantage of these edge and control devices. A hacked IoT device might send fake data to cloud servers or allow unwanted access to sensitive corporate papers, financial projections, and company data. This could result in equipment failure, as well as economic damage. One of the biggest issues in the smart industry is ensuring cybersecurity in the IIoT. It comprises protection from malicious software, prevention from unauthorized access, and communication and physical privacy protection. By installing modern and comprehensive security procedures, the IIoT's security, privacy, and trustworthiness may be improved. Deep learning (DL) techniques for designing and developing solutions related to cybersecurity have gained a lot of interest in recent years from both industry and academics. DL techniques have a lot of potential for producing improved results from industries' huge data [5]. Therefore, developing more safe and robust attack detection techniques for the IIoT continues to be a challenge. The remaining paper is composed of the following sections: Section 2 presents the related work. Section 3 presents the proposed hybrid CNN-LSTM model. Section 4 presents the results and discussion part, while Section 5 which is the Conclusions part.