"A 5G-Enabled AI-Based Malware Classification System for the Next Generation of Cybersecurity"
The Industrial Internet of Things (IIoT) is increasingly gaining popularity due to its ability to create communication networks between various components of an industry and usher in the new Industry 4.0 revolution. IIoT, powered by wireless 5G connectivity and Artificial Intelligence (AI), can analyze critical problems and provide solutions to improve the operational performance of manufacturing, healthcare, and other industries. The Internet of Things (IoT) is highly user-centric because it connects TVs, voice assistants, refrigerators, and other devices, whereas IIoT is concerned with improving the health, safety, or efficiency of larger systems, bridging hardware and software, and performing data analysis to provide real-time insights. While IIoT has many benefits, it also has some drawbacks, such as security threats in the form of attacks attempting to disrupt the network or extract resources. As IIoT becomes more prevalent in industries, it is critical to develop an efficient system to address such security concerns. A multinational team of researchers led by Professor Gwanggil Jeon at Incheon National University wanted to address this challenge. They explored the world of 5G-enabled IIoT to investigate its threats and devise a novel solution. The team presented an AI- and deep learning-based malware detection system for 5G-assisted IIoT systems in a recent review published in IEEE Transactions on Industrial Informatics. According to Professor Jeon, security threats can lead to operational or deployment failure in IIoT systems, resulting in high-risk situations. Therefore, they decided to investigate and compare existing research, identify gaps, and propose a new design for a security system capable of detecting and classifying malware attacks in IIoT systems. The team's system analyzes malware using a method called grayscale image visualization with a deep learning network. It then uses a multi-level Convolutional Neural Network (CNN) architecture to categorize the malware attack into different types. This security system is also integrated with 5G to enable low latency and high throughput sharing of real-time data and diagnostics. When compared to traditional system architectures, the new design achieved 97 percent accuracy on the benchmark data set. This article continues to discuss the research and development behind the new 5G-enabled AI-based malware classification system.