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Hybrid model of rule based and clustering analysis for big data security

机译：基于规则和聚类分析的混合模型

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The most of the organizations tend to accumulate the data related to security, which goes up-to terabytes in every month. They collect this data to meet the security requirements. The data is mostly in the shape of logs like Dns logs, Pcap files, and Firewall data etc. The data can be related to any communication network like cloud, telecom, or smart grid network. Generally, these logs are stored in databases or warehouses which becomes ultimately gigantic in size. Such a huge size of data upsurge the importance of security analytics in big data. In surveys, the security experts grumble about the existing tools and recommend for special tools and methods for big data security analysis. In this paper, we are using a big data analysis tool, which is known as apache spark. Although this tool is used for general purpose but we have used this for security analysis. It offers a very good library for machine learning algorithms including the clustering which is the main algorithm used in our work. In this work, we have developed a novel model, which combines rule based and clustering analysis for security analysis of big dataset. The dataset we are using in our experiment is the Kddcup99 which is a widely used dataset for intrusion detection. It is of MBs in size but can be used as a test case for big data security analysis.
机译：在大多数组织会积累与安全性，其上升到每个月兆兆字节的数据。他们收集这些数据，以满足安全要求。的数据是主要在如DNS日志，PCAP文件日志的形状，并且防火墙数据等的数据可以与如云，电信，或智能电网中的任何通信网络。通常，这些日志存储在数据库或仓库，其在尺寸上变得最终巨大。这样的数据规模庞大高潮大数据安全分析的重要性。在调查中，安全专家抱怨现有的工具，并建议使用专用工具和大数据安全分析方法。在本文中，我们使用的是大数据分析工具，这被称为Apache的火花。虽然该工具用于一般用途，但我们用它进行安全分析。它提供了机器学习算法，包括这是我们工作中使用的主要算法聚类一个非常好的图书馆。在这项工作中，我们已经开发了一种新的模式，它结合了基于规则和聚类分析大数据集的安全性分析。我们使用在我们的实验数据集是其KDDCUP99是入侵检测中广泛使用的数据集。它的大小MB的，但可以作为一个测试案例大数据安全分析。

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