No abstract

This paper analyzes the current real-time monitoring system based on grey-related IoT security sensors for the detection of risk factors in the production environment of the Internet of Things and proposes a design plan for the Internet of Things environment monitoring based on the grey-related Internet of Things security sensor network, and according to the reliability guarantee mechanism of the system, a three-dimensional uniform IoT node deployment method suitable for IoT security monitoring is proposed. Based on the grey correlation analysis, it can provide a quantitative measurement analysis for the development and change state of a system, which is very suitable for the analysis of dynamic operating systems. As a real-time dynamic system of the Internet of Things, the use of grey correlation method to analyze its network security status has good operability and practical value. According to the multisource information processing technology, the monitoring data are preprocessed by dynamic limiting filtering, and then the data are fused at the data level with the optimal weighting algorithm. Through the use of grey correlation analysis to quantify the relative impact of cyberattacks on the network within a certain period of time, the quantitative assessment of the security environment and status of the entire network is realized. Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision-level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.

In the information extraction, information sources can be screened according to the characteristics of the target network at the present stage, and the knowledge graph generated thereby can play a role in assisting the security analysis of the general network or power grid control network, mobile Internet and other special networks. In the method proposed in this paper, knowledge reasoning is mainly based on the attack conditions and attack methods to reason about the success rate and return of the attack. Through the obtained quality information, map construction information extraction and reasoning are performed to realize the correlation analysis of the information, and the information processing results are stored in the graphic structure. When analyzing the alerts generated by IDS, it is necessary to solve the multi-source alarm format generated by various devices produced by different suppliers. The attack diagram constructs the attack mode to guide the defense side to take targeted defense measures, and the attack success rate is used to judge the defense priority of all network nodes. After completing the construction of the graph, the attack graph is generated for the specific network environment under the guidance of the knowledge graph. In the process of attack graph generation, attack method and attack condition of attack instance can be used to guide the match of pre-condition and post-condition, so as to find the attack path. Attack success rate and attack profit attribute can be used to assist subsequent risk analysis. After simulation tests, the timeliness and availability of the system are verified, and this makes a contribution to the grid network management.

The emergence of network penetration attacks makes network security problems more and more prominent. To better detect network penetration attacks, this article first proposes an attack detection method based on ant colony classification rules mining algorithm based on swarm intelligence theory. Classification rules perform pattern matching to detect attack behavior. Second, the quantitative problem in the evaluation mechanism of power internet security vulnerability is analyzed. The addition and deletion of power internet of things (IoT) security assessment elements introduces a probabilistic computing model, improves the vulnerability assessment mechanism of power object networks, and solves the problem of power object network security and quantitative computing. Finally, after performing a series of preprocessing on the dataset, using the above method to find the classification rule or classification center, respectively, perform pattern matching or similarity calculation, and finally obtain the attack detection result. A series of evaluation functions are established in the experiment, and the experimental results are compared with other related algorithms. The results show that the method can effectively detect network penetration attacks and IoT security vulnerabilities, and the effectiveness of attack detection methods is greatly improved.

In view of the current network information data security transmission method in the process of data transmission, the abnormal data detection performance is poor and the attack rate is high in the process of data transmission. This paper proposes a network information data security transmission method based on the DES algorithm. The 3DES algorithm is used to complete the data encryption process, and the RSA encryption algorithm is used to encrypt the key to get the final data transmission object. The network model is constructed. The LOF algorithm is used to eliminate the abnormal data nodes, and the reliable network nodes are used to complete the data transmission. The experimental results show that this method can effectively reduce the missing rate of abnormal data, further improve the data resistance to attack, and promote the development of network data transmission technology.