大数据驱动下主动防御网络安全性评估技术

采用传统技术对安全性评估时受到外界干扰因素影响,使评估不精准,需引入网络熵对抗量化技术对大数据驱动下主动防御网络安全性进行评估,由此提高评估结果准确率。利用主动防御原理构建基于Petri网的安全性评估数学模型,模型中所体现攻击行为对网络造成间接影响,为降低间接影响造成的干扰需引入网络熵权衡收益,以提高网络使用安全性,通过权衡结果对主动防御网络安全性进行评估。实验结果表明,该评估技术准确率最高可达96%,用户可在大数据驱动下使用网络,保障用户个人信息安全。

Security assessment technology of active defense network driven by large data

When traditional technology is applied to the safety evaluation, it will be influenced by external interference factors, so that the evaluation is not accurate. It is necessary to introduce network entropy counter quantization technology to evaluate the safety of active defense network driven by big data, so as to improve the accuracy of evaluation results. Construction of Petri network security evaluation based on the mathematical model of the active defense principle, attack behavior reflects the model caused by indirect impact on the network, to reduce interference caused by indirect effects should be introduced to weigh the benefits in order to improve the network entropy, network security, by weighing the results of network active defense safety assessment. The experimental results show that the accuracy of the evaluation is up to 96%, and the user can use the network to ensure the security of the user"s personal information under the large data drive.