No abstract

Denial of service (DoS) attack is a typical and extremely destructive attack, which poses a serious threat to the Internet security and is highly concealed, making it difficult to detect. In response to this problem, the paper proposes an efficient DoS attack traffic detection method, Random Forest and Multilayer Perceptron hybrid network attack detection algorithm (RF-MLP). At first, it is adopted that the random forest algorithm can be used for feature selection and the optimal threshold can be determined by drawing a learning curve; therefore the optimal feature subset is determined. Then the optimal feature subset is used as the input of the multilayer perceptron for training. We will analyze the experimental results obtained using different configurations by varying the number of training neurons and the number of hidden layers of the multilayer perceptron network in order to improve the accuracy and reduce the number of false results. Using the real network traffic CICIDS2017 dataset and UNSW-NB15 dataset to evaluate the method in this paper, the results show that the model can effectively detect and classify DoS attacks, the accuracy rate can reach 99.83% and 93.51%, and there is also a significant reduction in the false alarm rate, verifying the effectiveness of the method and its ease of use.

The microgrid central controller (MGCC) integrates the functions of control, monitoring, and communication in microgrid system, and has powerful capabilities of information collection and data processing. However, with the development of microgrid system worldwide, the information security management capabilities of the MGCC are poor, If information / network attacks cannot be actively detected and identified, it will easily reduce the reliability of the microgird system operation. Attackers can use abnormal information or use the MGCC as a springboard to further attack the upper-layer system. Aiming at the above problems, this paper presents an attack detection method based on convolutional neural network, and a detailed design process of attack detection model of the MGCC is proposed. In the attack detection method, the important data streams in the MGCC are used as the input of the convolutional neural network model, then the convolutional neural network model detects or classifies these data streams, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.

Photovoltaic grid-connected interface devices are an important class of smart devices in microgrids. The authenticity and reliability of the data they acquire, as well as the safety and stability of operation, are related to the safe and reliable operation of the entire microgrid system. However, in the context of microgrid intelligence and informatization, information / network attacks will become the norm, making network-dependent information interaction methods subject to various security risks. The photovoltaic grid-connected interface device involves an open operating environment and is extremely vulnerable to network attacks. The attack information will occupy the space or resources of the photovoltaic grid-connected interface device, making the photovoltaic grid-connected interface device unable to respond to other important requests or instructions in a timely manner, and in severe cases, will cause the device to be paralyzed and affect the normal system operation. Aiming at the above problems, this paper presents an attack detection method based on the gradient-upgraded decision tree model, and gives a detailed design process of attack detection model of the photovoltaic grid-connected interface device. That is, the important data flow in the photovoltaic grid-connected interface device is used as the input of the gradient-upgraded decision tree model, and then the gradient-upgraded decision tree model detect or classify flows, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.

With the promotion of the concepts of “energy Internet” and “multi energy complementation”, the combined cold heat and power (CCHP) system technology has been paid more and more attention by the energy power circles at home and abroad. CCHP system generates electricity through internal combustion engine, meanwhile, the waste heat discharged after power generation is supplied with heat and cooling to users through waste heat recovery equipment, so as to effectively improve the energy utilization rate. However, with the development of information and intelligence in the power grid, the threat of various network malicious attacks on the power industrial control terminal is growing. The traditional CCHP system grid connection interface device operating environment is open and vulnerable to network malicious attacks. In this paper, an interface device of CCHP based on Elman neural network algorithm is proposed. The technical problem to be solved is to be able to realize active defense against unknown attacks and improve the security and operation reliability of CCHP system.