基于改进C-SVC的工控网络安全态势感知

工控网络攻击类型多样、强度不一,在这种情况下,传统检测技术无法对多种类型的攻击进行有效识别,也无法给出全面准确的工控网络安全态势.为此,提出工控网络安全态势感知模型:首先采取改进的C-SVC算法对多源数据进行规则提取;然后利用决策融合算法进行决策层融合,获取最终态势感知结果.实验结果表明:所提出的模型和算法能够有效地识别多类型攻击,准确判断出系统遭受到的攻击,并形成态势感知结果.

Industrial control network security situation awareness based on improved C-SVC

The attacks against the industrial control network have different types and various attack intensity. Under this circumstance, the traditional detection techniques cannot identify the multiple types of attacks effectively, and can not assess the security situations of the industrial control network comprehensively and accurately. Therefore, the industrial control network security situation awareness model is proposed. Firstly, the rule extraction can be done by applying the improved C-SVC algorithm to the multi-sensor data. Then with the application of decision fusion algorithm, the decision-level fusion is completed and the results of situation awareness are procured. The simulation experiment results show that the proposed model and algorithms can distinguish multiple types of attacks effectively, identify the attacks that are launched against the industrial control system accurately, and generate the results of situation awareness.