# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 1 Introduction  
Concomitant with advances in information and communication technology (ICT), the Industrial Internet of Things (IIoT) has developed rapidly. However, because IIoT is an interconnected and open approach, it is highly vulnerable to malware attacks [1; 2; 3]. Furthermore, as the IIoT sensors used for the industrial control systems (ICSs) [4] of smart factories are directly connected to the Internet, they are also vulnerable to cyberattacks such as malware, distributed denial of service (DDoS), brute-force attack, man-in-the-middle attack, SQL injection, data exfiltration, and phishing [5; 6; 7], threatening the normal operation of ICSs [8; 9]. Consequently, accurate and efficient detection and classification of malware is critical for smart factory cybersecurity. Smart sensors connected to the automated production network in the manufacturing facility process [10] are the core driving forces of the manufacturing industry, and IIoT is being installed in major industrial infrastructure [11; 12]. Such IIoT enables communication between sensors and control systems as well as the monitoring and control of manufacturing systems in production lines and manufacturing processes. Furthermore, artificial intelligence (AI) technologies such as big data analysis and deep learning are being used to process and analyze data from various sources and perform advanced predictive analysis including defect level prediction, maintenance forecasting, and demand forecasting [13]. According to GE Digital, IIoT will enable manufacturing cost savings and benefit 46% of the global economy [14]. Additionally, International Data Corporation (IDC), an IT market research firm, has predicted that by 2025, 41.6 billion Internet of Things (IoT) devices will be connected, generating 794 zettabytes (ZB) of data [15]. As such, multifarioustypes of data are being generated following an increase in the use of IIoT devices in the manufacturing industry. The types and severity of cybersecurity threats through IIoT are also increasing proportionally with the adoption of IIoT in smart factories. Increasing cyberattacks against the supervisory control and data acquisition (SCADA), programmable logic controller (PLC), human-machine interface (HMI), remote terminal unit (RTU), and distributed control system (DCS) sensors, as well as intelligent electronic devices (IEDs) in Shodan, where IIoT devices connected to the Internet can be indexed and identified, verify that IIoT devices in the manufacturing industry connected to the Internet are targets for hackers. This makes manufacturing one of the industries that is most vulnerable to cyberattacks, and failure to upgrade or patch IIoT firmware in a timely manner leaves IIoT devices vulnerable to malicious attacks. This is because it is practically difficult to implement a security module for IoT terminals owing to the resource constraints of IIoT devices, such as low-capacity computational power. As manufacturers, communication, networking, data processing, data storage capacity, computational power, and transmission capabilities of IIoT devices vary, it is difficult to apply an existing security module or a unified security method [16]. Therefore, it is necessary to develop a security mechanism that can accurately detect and respond to malware for IIoT devices applied to smart factories. It is difficult to achieve accurate intrusion detection in real time in the smart factory IIoT environment [17].