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Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis
Security and Communication Networks ( IF 1.968 ) Pub Date : 2021-06-17 , DOI: 10.1155/2021/9927325
F. F. Hu 1 , S. T. Zhang 1 , X. B. Lin 1 , L. Wu 1 , N. D. Liao 2
Affiliation  

With the continuous development of smart grids, communication networks carry more and more power services, and at the same time, they are also facing more and more security issues. For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security of smart grids. At present, the classification of network traffic mainly depends on the correct extraction of network protocol characteristics. However, the process of extracting network features by some traditional methods is time-consuming and overly dependent on experience. In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data statistical analysis. This method can simultaneously extract the time characteristics between different packet groups and the spatial characteristics in the same packet group. Therefore, it greatly saves manpower and gets rid of the dependence on experience value. The proposed method has been tested and verified on the UNSW-NB15 dataset and the real dataset collected by the power company. The results show that the proposed method can correctly classify abnormal network flows and is much better than traditional machine learning methods. In large-scale real network flow scenarios, the detection rate of the proposed method exceeds 97%, while the traditional method is generally less than 90%.

中文翻译:

基于GACNN和数据统计分析的智能电网异常流量分类

随着智能电网的不断发展,通信网络承载着越来越多的电力服务,同时也面临着越来越多的安全问题。例如,一些恶意软件通常使用加密技术或隧道技术绕过防火墙、入侵检测系统等,从而对智能电网的信息安全构成严重威胁。目前,网络流量的分类主要依赖于网络协议特征的正确提取。然而,一些传统方法提取网络特征的过程耗时且过于依赖经验。为了解决电网流量的准确分类问题,本文提出了一种基于遗传算法优化(GACNN)和数据统计分析的卷积神经网络方法。该方法可以同时提取不同分组组之间的时间特征和同一分组组内的空间特征。因此,大大节省了人力,摆脱了对经验值的依赖。所提出的方法已经在UNSW-NB15数据集和电力公司收集的真实数据集上进行了测试和验证。结果表明,所提方法能够正确分类异常网络流,明显优于传统的机器学习方法。在大规模真实网络流场景中,所提方法的检测率超过97%,而传统方法普遍低于90%。该方法可以同时提取不同分组组之间的时间特征和同一分组组内的空间特征。因此,大大节省了人力,摆脱了对经验值的依赖。所提出的方法已经在UNSW-NB15数据集和电力公司收集的真实数据集上进行了测试和验证。结果表明,该方法能够正确分类异常网络流,明显优于传统的机器学习方法。在大规模真实网络流场景中,所提方法的检测率超过97%,而传统方法普遍低于90%。该方法可以同时提取不同分组组之间的时间特征和同一分组组内的空间特征。因此,大大节省了人力,摆脱了对经验值的依赖。所提出的方法已经在UNSW-NB15数据集和电力公司收集的真实数据集上进行了测试和验证。结果表明,该方法能够正确分类异常网络流,明显优于传统的机器学习方法。在大规模真实网络流场景中,所提方法的检测率超过97%,而传统方法普遍低于90%。大大节省人力,摆脱对经验值的依赖。所提出的方法已经在UNSW-NB15数据集和电力公司收集的真实数据集上进行了测试和验证。结果表明,该方法能够正确分类异常网络流,明显优于传统的机器学习方法。在大规模真实网络流场景中,所提方法的检测率超过97%,而传统方法普遍低于90%。大大节省人力,摆脱对经验值的依赖。所提出的方法已经在UNSW-NB15数据集和电力公司收集的真实数据集上进行了测试和验证。结果表明,该方法能够正确分类异常网络流,明显优于传统的机器学习方法。在大规模真实网络流场景中,所提方法的检测率超过97%,而传统方法普遍低于90%。
更新日期:2021-06-17
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