Deep Learning Approach for Intelligent Intrusion Detection System
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Abstract
The Industrial Internet of Things has grown significantly in recent years. While implementing industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the complex and varied industrial Internet of Things environment provided a new attack surface for network attackers. As a result, conventional intrusion detection technology cannot satisfy the network threat discovery requirements in today’s Industrial Internet of Things environment.
An intrusion detection system (IDS) is a critical component of network security protection because it enables the system to detect network intrusions efficiently. However, in recent years, as the operating environment and structure of the Industrial Internet of Things have changed, traditional intrusion detection models (such as intrusion detection models based on simple machine learning) have been unable to provide adaptive detection, response, and defence against complex network attacks.
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