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首页> 外文期刊>Performance evaluation review >DeepSec meets RawPower - Deep Learning for Detection of Network Attacks Using Raw Representations
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DeepSec meets RawPower - Deep Learning for Detection of Network Attacks Using Raw Representations

机译：DeepSec满足RawPower-使用原始表示的深度学习以检测网络攻击

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摘要

The application of machine learning models to the analysis of network traffic measurements has largely grown in recent years. In the networking domain, shallow models are usually applied, where a set of expert handcrafted features are needed to fix the data before training. There are two main problems associated with this approach: firstly, it requires expert domain knowledge to select the input features, and secondly, different sets of custom-made input features are generally needed according to the specific target (e.g., network security, anomaly detection, traffic classification). On the other hand, the power of machine learning models using deep architectures (i.e., deep learning) for networking has not been yet highly explored. In this paper we explore the power of deep learning models on the specific problem of detection of network attacks, using different representations for the input data. As a mayor advantage as compared to the state of the art, we consider raw measurements coming directly from the stream of monitored bytes as the input to the proposed models, and evaluate different raw-traffic feature representations, including packet and flow-level ones.
机译：近年来，机器学习模型在网络流量测量分析中的应用已大大增加。在网络领域，通常使用浅层模型，在训练之前，需要一组专家手工制作的功能来修复数据。这种方法有两个主要问题：首先，它需要专业的领域知识来选择输入特征，其次，根据特定目标（例如，网络安全，异常检测），通常需要不同的定制输入特征集。 ，流量分类）。另一方面，尚未高度探索使用深度架构（即深度学习）进行联网的机器学习模型的功能。在本文中，我们使用输入数据的不同表示形式来探索深度学习模型在检测网络攻击的特定问题上的强大功能。作为与现有技术相比的市长优势，我们将直接来自监视字节流的原始测量结果视为所建议模型的输入，并评估了不同的原始流量特征表示，包括数据包和流量级别的表示。

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