关键词: 智慧园区, 综合能源系统, 微电网, 网络攻击, 关联规则分析与匹配, 灰色关联分析, 攻击防护策略, 负荷频率控制

Abstract:

Control system of smart parks at all levels are vulnerable to network attacks. In order to improve the system's coping capacities to network attacks and the identification accuracy and efficiency of malicious attack events,a network attack association analysis method for smart park systems is proposed. The method takes Frequent Pattern-Growth（FP-Growth） algorithm to detect the association rules of abnormal network attack events. FP-Growth algorithm can quickly mine the frequent item sets of abnormal events and train the association rules. Grey correlation analysis algorithm generates the abnormal manifestation set of real-time abnormal network attack events. Then, following the association rules,the network attack scenarios can be recognized on-line. Network attack protection strategy is made according to the links corresponding to the different attack scenarios to eliminate the impact of the attack. Finally,the efficiency of the proposed method in training association rules is verified by data of different volumes,and the feasibility of the network attack protection strategy is verified by the Load Frequency Control module of the smart park system.

Key words: smart park, integrated energy system, micro-grid, network attack, association rule analysis and matching, grey correlation analysis, attack protection strategy, control on load and frequency