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Real-Time Analysis of Big Network Packet Streams by Learning the Likelihood of Trusted Sequences

机译：通过了解可信序列的可能性对大型网络数据包流进行实时分析

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摘要

Deep Packet Inspection (DPI) is a basic monitoring step for intrusion detection and prevention, where the sequences of packed packets are to be unpacked according to the layered network structure. DPI is performed against overwhelming network packet streams. By nature, network packet data is big data of real-time streaming. The DPI big data analysis, however are extremely expensive, likely to generate false positives, and less adaptive to previously unknown attacks. This paper presents a novel machine learning approach to multithreaded analysis for network traffic streams. The contribution of this paper includes (1) real-time packet data analysis, (2) learning the likelihood of trusted and untrusted packet sequences, and (3) improvement of adaptive detection against previous unknown intrusive attacks.
机译：深度数据包检查（DPI）是用于入侵检测和预防的基本监视步骤，其中打包的数据包序列将根据分层的网络结构进行解包。针对压倒性的网络数据包流执行DPI。从本质上讲，网络数据包数据是实时流传输的大数据。但是，DPI大数据分析非常昂贵，可能会产生误报，并且对先前未知的攻击的适应性较差。本文提出了一种新颖的机器学习方法，用于网络流量流的多线程分析。本文的贡献包括（1）实时数据包数据分析，（2）了解可信和不可信数据包序列的可能性，以及（3）改进针对先前未知入侵攻击的自适应检测。

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