Mathematical Modeling of Communication Data Encryption Protection and Intelligent Identification of Network Intrusion

The information security and network intrusion problems in today's communication systems are becoming increasingly severe. To this end, this paper introduces a comprehensive solution based on encryption algorithms and intelligent recognition technology, aiming to effectively improve the confidentiality of communication data and the intrusion detection accuracy of the recognition system. First, in response to the encryption problem of communication data, this paper introduces an encryption model based on ECC (Elliptic Curve Cryptography), which uses its smaller key length and efficient computing characteristics to ensure data security. Secondly, in order to enhance the ability to identify network intrusions, this paper designs an intrusion detection model based on deep learning, which uses a combination of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) to effectively improve the ability to identify diverse attack patterns. The model is trained on a large amount of network traffic data to establish an efficient feature extraction and classification system to detect potential intrusion threats in real time. Finally, by introducing an attack and defense model based on game theory, the defense strategy for network intrusion is optimized, taking into account the dynamic game process between attackers and defenders to maximize the defense effect and minimize system overhead.