Detecting threats on the Internet is a key factor in maintaining data and information security. An intrusion detection system tries to prevent such attacks from occurring through the analysis of patterns and behavior of the data stream in the network. This paper presents a large data stream detection and analysis distributed platform, through the use of machine learning to dimensionality reduction. The system is evaluated based on three criteria: the accuracy, the number of false positives, and number of false negatives. Each classifier presented better accuracy when using 5 and 13 features, having fewer false positives and false negatives, allowing the detection of threats in real-time over a large volume of data, with greater precision.

中文翻译：

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使用深度学习改善网络中的威胁检测

检测Internet上的威胁是维护数据和信息安全的关键因素。入侵检测系统试图通过分析网络中数据流的模式和行为来防止此类攻击的发生。本文提出了一种大型数据流检测和分析的分布式平台，通过使用机器学习来降维。系统基于以下三个标准进行评估：准确性，误报数量和误报数量。当使用5和13功能时，每个分类器都呈现出更高的准确性，误报率和误报率都更少，从而能够以更高的精度实时检测大量数据中的威胁。