基于大数据技术的交互式网络恶意入侵行为检测方法

Interactive Network Malicious Intrusion Behavior Detection Method Based on Big Data Technology

为提高交互式网络的安全性,优化现有网络入侵检测方法,引进大数据技术,以交互式网络为例,设计一种针对网络入侵行为的全新检测方法。首先,绘制基于大数据技术的网络恶意入侵行为特征提取流程图,进行网络数据的归一化与标准化处理;其次,计算信息流密集度,对恶意入侵行为在网络中表现的多种状态进行识别;最后,从模糊分析角度对入侵行为进行聚类,输出聚类结果,完成网络恶意入侵行为的检测。实验结果表明,该方法可以实现对网络中异常行为数据的高精度检测,检测率最高可以达到99.42%,能够为网络安全运营提供更好的保障。

In order to improve the security of the interactive network, optimize the existing network intrusion detection method,introduce big data technology, take the interactive network as an example, design a new detection method for network intrusion behavior. Firstly, the flow chart of network malicious intrusion feature extraction based on big data technology is drawn to normalize and standardize the network data;Secondly, the information flow density is calculated to identify the various states of malicious intrusion in the network;Finally, the intrusion behavior is clustered from the perspective of fuzzy analysis, and the clustering results are output to complete the detection of network malicious intrusion behavior. Experimental results show that this method can achieve high-precision detection of abnormal behavior data in the network, and the highest detection rate can reach 99.42%, which can provide better guarantee for network security operation.