scholarly journals A Comprehensive Game Theoretical Defense Strategy in Demand Side Management Against Price Tampering Attacks

2021 ◽  
Vol 9 ◽  
Author(s):  
Rong Fu ◽  
Jie Chen ◽  
Xiaofei Zhang ◽  
Jie Zhang

Price tamping attacks may cause market turbulence, attack detection and defense strategy are needed to study. Firstly, demand response characteristics are analyzed in a User Energy System. A quantitative model is established to describe the load changes caused by price tampering attacks. Secondly, a space-based cumulative intrusion detection method is proposed to pick up the discrepancy under tampering attacks. To verify the practicability of the proposed method, intrusion detection experiments are tested in the Principal Information and Safety Laboratory. Then, comprehensively considering the purchase of electricity from the power grid, self-generation, and load shedding, a quantitative model of attack consequences is established based on the allocation coefficient. Thus, the intrusion detection algorithm is used as a defense resource, and a demand-side defense protection strategy is formed to find an optimal deployment method based on non-cooperative game theory. The defensive protection strategy takes the quantitative model of attack consequences as the solution target, and solves the Nash equilibrium solution under different attack modes. Finally, in the IEEE-33 node system simulations, the defense resource is deployed using intrusion detection strategy, and the defense decision is executed to show the effectiveness of the comprehensive protection strategies.

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.


2020 ◽  
Vol 213 ◽  
pp. 02038
Author(s):  
Peng Fang ◽  
Cui Mao ◽  
Yuping Chen ◽  
Shan Zhou ◽  
Rui You ◽  
...  

As the physical carrier of the energy Internet, the integrated energy system has become the focus of current research. Considering the renewable energy and demand side load fluctuations, using the price type and the alternative demand side response characteristics, a day-ahead and intraday optimization scheduling model that takes into account the demand side response is established, in which the intraday, according to the difference of electricity, cold/heat and natural gas scheduling time, a three-layer rolling optimization scheduling model is proposed. The example analysis shows that this model can suppress the fluctuation of renewable energy and load in the day, improve the stability of the system, and further reduce the operating cost of the system.


Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 119505
Author(s):  
Yunfeng Li ◽  
Wenli Xue ◽  
Ting Wu ◽  
Huaizhi Wang ◽  
Bin Zhou ◽  
...  

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.


2016 ◽  
Vol 21 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Huan Ma ◽  
Hao Ding ◽  
Yang Yang ◽  
Zhenqiang Mi ◽  
James Yifei Yang ◽  
...  

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