Network security situation assessment (NSSA) is an important and effective active defense technology in the field of network security situation awareness. By analyzing the historical network security situation awareness data, NSSA can evaluate the network security threat and analyze the network attack stage, thus fully grasping the overall network security situation. With the rapid development of 5G, cloud computing, and Internet of things, the network environment is increasingly complex, resulting in diversity and randomness of network threats, which directly determine the accuracy and the universality of NSSA methods. Meanwhile, the indicator data is characterized by large scale and heterogeneity, which seriously affect the efficiency of the NSSA methods. In this paper, we design a new NSSA method based on the autoencoder (AE) and parsimonious memory unit (PMU). In our novel method, we first utilize an AE-based data dimensionality reduction method to process the original indicator data, thus effectively removing the redundant part of the indicator data. Subsequently, we adopt a PMU deep neural network to achieve accurate and efficient NSSA. The experimental results demonstrate that the accuracy and efficiency of our novel method are both greatly improved.

中文翻译：

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一种基于AE和PMU的高效网络安全态势评估方法

网络安全态势评估（NSSA）是网络安全态势感知领域中一项重要且有效的主动防御技术。通过分析历史网络安全态势感知数据，NSSA可以评估网络安全威胁，分析网络攻击阶段，从而全面掌握网络安全态势。随着5G、云计算、物联网的快速发展，网络环境日益复杂，导致网络威胁的多样性和随机性，直接决定了NSSA方法的准确性和通用性。同时，指标数据具有规模大、异质性强的特点，严重影响了NSSA方法的效率。在本文中，我们设计了一种基于自编码器 (AE) 和简约存储单元 (PMU) 的新 NSSA 方法。在我们的新方法中，我们首先利用基于 AE 的数据降维方法来处理原始指标数据，从而有效地去除指标数据的冗余部分。随后，我们采用 PMU 深度神经网络来实现准确高效的 NSSA。实验结果表明，我们的新方法的准确性和效率都大大提高。