Advancing Industrial Internet of Things Cybersecurity with Artificial Intelligence

The University’s research, titled ‘A Multi-Layer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT,’ details the cutting-edge AI- and deep learning-based malware detection system that looks to safeguard the Industrial Internet of Things from cyber-attacks, the Innovation News Network reported.

In recent years, the Industrial Internet of Things has gained traction because of its ability to create novel communication networks between different aspects of industry and power the evolution to Industry 4.0.

The Industrial Internet of Things is powered by wireless 5G connectivity, and AI is able to examine and resolve critical problems that enhance the operational performance of industries, such as manufacturing and healthcare.

Whereas the Internet of Things is user-centric, connecting televisions, voice assistants, and refrigerators, for example, the Industrial Internet of Things optimises the health, safety, and efficiency of larger systems, connecting hardware with software and performing data analysis to provide insights in real-time.

Despite the various benefits of the Industrial Internet of Things, it also carries a range of vulnerabilities, including security threats such as attacks that disturb the network or drain resources. Due to the technology becoming increasingly popular, innovating an efficient system to deal with such threats is essential.

Professor Gwanggil Jeon from Incheon National University, said, “Security threats can often lead to operation or deployment failure in Industrial Internet of Things systems, which can create high-risk situations. So, we decided to investigate and compare available research, find out the gaps, and propose a new design for a security system that can not only detect malware attacks in The Industrial Internet of Things systems but also classify them.”

The new system created by the team utilises a method known as grayscale image visualisation with a deep learning network for monitoring the malware. It also applies a multi-level convolutional neural network (CNN) architecture to classify malware attacks into different types. They also integrated 5G into the system, which enables low latency and high throughput sharing of data and diagnostics in real time.

The novel design demonstrated an improved accuracy that achieved 97% on the benchmark dataset. They discovered the reason for this accuracy is due to the system’s ability to extract complementary discriminative features by amalgamating multiple layers of information.

The new system can be employed to safeguard real-time connectivity applications, such as smart cities and autonomous vehicles, providing a robust framework for pioneering future advanced security systems to mitigate a range of cybercrime.

Professor Jeon concluded, “AI-based technology has dramatically changed our lives. Our system harnesses the power of AI to enable industries to recognise miscreants and prevent the entry of unreliable devices and systems in their Industrial Internet of Things networks.”

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