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Fisheries IoT realizes real-time online detecting and precise modulation of aquaculture process, which greatly enhances the informationization, automation and intelligence level of farms, reduces production costs, promotes the scale of fishery industry, and improves the quality level and market competitiveness of agricultural products. However, the key to large-scale fishery farming lies in accurate water environment modulation, the IoT gateway system is the key to realize the ability of intelligent fishery to achieve accurate water environment modulation. Once the aquatic product networking is attacked by malicious IoT commands, it may cause rapid disintegration of the farming environment ecosystem at any time, resulting in huge economic losses. Due to the size and power consumption of fishery IoT gateways, traditional security protection means are often difficult to be applied in such scenarios. Therefore, this paper proposes an improved malicious command detection method for smart fisheries sensor gateways based on deep learning techniques, aiming to accurately and efficiently identify malicious control commands of smart fisheries sensor networks on IoT gateways. The main contributions of this paper are three: (1) Transforming various types of signals from gateways into feature texts, which are used as inputs for deep learning for command classification. (2) Based on the BiLSTM model, it is improved by adding a multi-headed self-attention mechanism to obtain a smaller size and higher accuracy malicious command detection model applicable to smart fisheries gateways (3) Experimental results show that compared with other models, highly confusing malicious commands elaborated for the command characteristics of short, small size and high change rate of smart fisheries sensor commands The improved BiLSTM model has a higher recognition rate.
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