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Deceptive attack behavior can interfere with the judgment ability of industrial control networks to transmit information, causing risky data to enter network hosts and leading to a decrease in network security. To avoid the occurrence of the above situation, design an industrial control network spoofing attack behavior detection system based on adversarial machine learning. Set up three types of sub module units for attack behavior collection, processing, and detection verification, and complete the functional module design of the deception attack behavior detection system. Define attack behavior in adversarial machine learning algorithms, and based on this, extract features of deceptive attack behavior to achieve recognition of attack behavior. Analyze the security risks of industrial control networks, establish risk measurement conditions for joint deceptive attack behaviors, define specific detection modeling standards, and thus achieve the detection of information on deceptive attack behaviors in industrial control networks. The experimental results show that the application of the design method can detect deceptive attack information based on the difference in transmission wavelength of data samples, and the recall test results are between 0.93 and 0.98, indicating that the design method can accurately detect deceptive attack behavior, ensuring the operational security of industrial control networks.