Development and application of biological intelligence technology in computer

To study the development and application of biological intelligence technology in computers and realize high-precision network anomaly detection, a distributed intrusion detection system based on agent biological intelligence technology is proposed. The intrusion detection model is implemented based on commonly used traditional machine learning methods. According to the detection index data, the detection effect of each algorithm is analyzed. With the support of the overall structure of the system, analyze the control center, network host, partition control center, and Agent library; adopt corresponding response strategies according to the response rules in the response library, and use the communication module to timely judge whether the intrusion behavior is abnormal, using S5720S-28P-SI -AC24-port full-gigabit three-layer network management enterprise-level network core switch for data exchange; select AD2032 type alarm responder, which can monitor foreign intrusion; through V1.2 green computer information detector, system memory and drive disk All-round evaluation; analyze the realization mode, communication message format and communication protocol of the subject communication, design the data movement process based on the agent; realize the intrusion detection model with the help of the commonly used traditional machine learning method. According to the detection index data, the detection effect of each algorithm is analyzed. The experimental results show that, for the IDA system, when the first five clients suffer intrusion behavior, its detection function can check the abnormal situation in time. When the detection time is in the 40s, the detection accuracy is 70%. When the last five clients suffered intrusion behavior, their detection function was not perfect. When the detection time was 25s, the accuracy no longer increased, and the detection accuracy remained at 40% until 40s. When the first five clients of the Agent-based system suffer from intrusion, the detection accuracy is always high, up to 99%. When the last five clients suffer from intrusion, the detection accuracy can reach 95%. Therefore, the detection accuracy of this system is high. It is proved that the system has obvious advantages in terms of accuracy, which successfully proves that it can effectively solve the problem of network anomaly detection