论文标题
CPS攻击检测在有限的网络安全中的本地信息下：多节点多级分类合奏方法
CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approach
论文作者
论文摘要
网络安全漏洞是分布式网络物理系统（CPS）的常见异常。但是，即使使用尖端人工智能（AI）方法，网络安全漏洞分类仍然是一个困难的问题。在本文中，我们研究了网络安全性的多类分类问题，以进行攻击检测。考虑了一个具有挑战性的多节点数据审查案例。在这种情况下，当本地数据不完整时，每个数据中心/节点中的数据都无法共享。特别是，本地节点仅包含多个类别的一部分。为了培训全球多级分类器而不在所有节点上共享原始数据，我们研究的主要结果是设计多节点多级分类集合方法。通过从每个局部节点收集二进制分类器和数据密度的估计参数，每个局部节点的丢失信息都可以完成，以构建全局多类分类器。进行数值实验以验证多节点数据审查情况下所提出的方法的有效性。在这种情况下，我们甚至表明了对全数据A的拟议方法的表现。
Cybersecurity breaches are the common anomalies for distributed cyber-physical systems (CPS). However, the cyber security breach classification is still a difficult problem, even using cutting-edge artificial intelligence (AI) approaches. In this paper, we study the multi-class classification problem in cyber security for attack detection. A challenging multi-node data-censoring case is considered. In such a case, data within each data center/node cannot be shared while the local data is incomplete. Particularly, local nodes contain only a part of the multiple classes. In order to train a global multi-class classifier without sharing the raw data across all nodes, the main result of our study is designing a multi-node multi-class classification ensemble approach. By gathering the estimated parameters of the binary classifiers and data densities from each local node, the missing information for each local node is completed to build the global multi-class classifier. Numerical experiments are given to validate the effectiveness of the proposed approach under the multi-node data-censoring case. Under such a case, we even show the out-performance of the proposed approach over the full-data approach.
