Biblio

In order to improve production and management efficiency, traditional industrial control systems are gradually connected to the Internet, and more likely to use advanced modern information technologies, such as cloud computing, big data technology, and artificial intelligence. Industrial control system is widely used in national key infrastructure. Meanwhile, a variety of attack threats and risks follow, and once the industrial control network suffers maliciously attack, the loss caused is immeasurable. In order to improve the security and stability of the industrial Internet, this paper studies the industrial control network traffic threat identification technology based on machine learning methods, including GK-SVDD, RNN and KPCA reconstruction error algorithm, and proposes a heuristic method for selecting Gaussian kernel width parameter in GK-SVDD to accelerate real-time threat detection in industrial control environments. Experiments were conducted on two public industrial control network traffic datasets. Compared with the existing methods, these methods can obtain faster detection efficiency and better threat identification performance.

With the latest development of hydroelectric power generation system, the industrial control network system of hydroelectric power generation has undergone the transformation from the dedicated network, using proprietary protocols to an increasingly open network, adopting standard protocols, and increasing integration with hydroelectric power generation system. It generally believed that with the improvement of the smart grid, the future hydroelectric power generation system will rely more on the powerful network system. The general application of standardized communication protocol and intelligent electronic equipment in industrial control network provides a technical guarantee for realizing the intellectualization of hydroelectric power generation system but also brings about the network security problems that cannot be ignored. In order to solve the vulnerability of the system, we analyze and quantitatively evaluate the industrial control network of hydropower generation as a whole, and propose a set of attack and defense strategies. The method of vulnerability assessment with high diversity score proposed by us avoids the indifference of different vulnerability score to the greatest extent. At the same time, we propose an optimal attack and defense decision algorithm, which generates the optimal attack and defense strategy. The work of this paper can distinguish the actual hazards of vulnerable points more effectively.

This paper proposes a new random forest classification algorithm based on differential privacy protection. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed in this paper. The hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Experiment results show that the random forest algorithm based on differential privacy can provide high privacy protection while ensuring high classification performance, achieving a balance between privacy and classification accuracy, and has practical application value.