Security Attitude Prediction Model of Secret-Related Computer Information System Based on Distributed Parallel Computing Programming

In recent years, there has been an upward trend in the number of leaked secrets. Among them, secret-related computers or networks are connected to the Internet in violation of regulations, cross-use of mobile storage media, and poor security management of secret-related intranets, which are the main reasons for leaks. Therefore, it is of great significance to study the physical isolation and protection technology of classified information systems. Physical isolation is an important part of the protection of classified information systems, which cuts off the possibility of unauthorized outflow of information from the network environment. To achieve the physical isolation of the network environment and build a safe and reliable network, it is necessary to continuously improve the level of network construction and strengthen network management capabilities. At present, the realization of physical isolation technology mainly relies on security products such as firewall, intrusion detection, illegal outreach, host monitoring, and auditing. This study analyzes network security systems such as intrusion detection, network scanning, and firewall. Establishing a model based on network security vulnerabilities and making up for network security hidden dangers caused by holes are generally a passive security system. In a network, the leader of network behavior—human behavior—needs to be constrained according to the requirements of the security management system and monitoring. Accordingly, this study proposes a security monitoring system for computer information network involving classified computer. The system can analyze, monitor, manage, and process the network behavior of the terminal computer host in the local area network, to achieve the purpose of reducing security risks in the network system. Based on the evaluation value sequence, the initial prediction value sequence is obtained by sliding adaptive triple exponential smoothing method. The time-varying weighted Markov chain is used for error prediction, the initial prediction value is corrected, and the accuracy of security situation prediction is improved. According to the security protection requirements of secret-related information systems, a complete, safe, reliable, and controllable security protection system for secret-related information systems is constructed, and the existing security risks and loopholes in secret-related information systems are eliminated to the greatest extent possible. This enables the confidentiality, integrity, and availability of confidential data and information in the computer information system to be reliably protected.