Network data collection is a vital part in the process of network monitoring, traffic billing, network management and intrusion detection. As a new kind of network architecture, Software Defined Network (SDN) provides a possibility of intelligent and adaptive network data collection with centralized control and programming. However, existing literatures lack a concrete solution to economically collect network data, while satisfying the quality of data processing and analytics. Current data collection methods are not sufficiently adaptive and intelligent in terms of network context awareness. In this paper, we propose an adaptive network data collection system in SDN by automatically selecting proper data collection nodes based on network status in a dynamic way. During data collection, network traffic is sampled by considering flow characteristics in order to effectively reduce the amount of collected data while ensuring the accuracy of later data analysis, e.g., malicious traffic detection. A series of experiments are conducted to test and verify the data collection system and show its advantages through comparison with existing works in terms of CPU/memory consumption, storage usage, flow size recovery, and threat perception.

中文翻译：

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SDN中的自适应网络数据采集系统

网络数据采集是网络监控、流量计费、网络管理和入侵检测过程中的重要环节。软件定义网络（SDN）作为一种新型的网络架构，为集中控制和编程的智能自适应网络数据采集提供了可能。然而，现有文献缺乏一个具体的解决方案来经济地收集网络数据，同时满足数据处理和分析的质量。当前的数据收集方法在网络上下文感知方面不够自适应和智能。在本文中，我们通过基于网络状态以动态方式自动选择合适的数据收集节点，提出了 SDN 中的自适应网络数据收集系统。在数据收集过程中，通过考虑流量特征对网络流量进行采样，以有效减少采集的数据量，同时保证后期数据分析的准确性，例如恶意流量检测。进行了一系列实验来测试和验证数据采集系统，并通过与现有工作在CPU/内存消耗、存储使用、流量大小恢复和威胁感知等方面进行比较来展示其优势。