By growing the number of Internet users and the prevalence of web applications, we have to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems, which consequently leads to an increase in the cyber and, in particular, zero-day attacks. The cost of generating appropriate signatures for these attacks is a potential motive for using machine learning-based methodologies. Although there exist many studies on the use of learning-based methods for attack detection, they generally use extracted features and overlook raw contents. This approach can lessen the performance of detection systems against content-based attacks like SQL injection, Cross-site Scripting (XSS), and various viruses. As a new paradigm, in this work, we propose a scheme, called deep intrusion detection (DID) system that uses the pure content of traffic flows in addition to traffic metadata in the learning and detection phases. To this end, we employ deep learning techniques recently developed in the machine learning community. Due to the inherent nature of deep learning, it can process high dimensional data content and, accordingly, discover the sophisticated relations between the auto extracted features of the traffic. To evaluate the proposed DID system, we use the ISCX IDS 2017 dataset. The evaluation metrics, such as precision and recall, reach $0.992$ and $0.998$, respectively, which show the high performance of the proposed DID method.

中文翻译：

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基于内容的深度入侵检测系统

随着 Internet 用户数量的增加和 Web 应用程序的流行，我们必须处理网络中非常复杂的软件和应用程序。这导致系统中出现越来越多的新漏洞，从而导致网络攻击的增加，尤其是零日攻击。为这些攻击生成适当签名的成本是使用基于机器学习的方法的潜在动机。尽管有许多关于使用基于学习的方法进行攻击检测的研究，但它们通常使用提取的特征并忽略原始内容。这种方法会降低检测系统对基于内容的攻击（如 SQL 注入、跨站点脚本 (XSS) 和各种病毒）的性能。作为一种新的范式，在这项工作中，我们提出了一个方案，称为深度入侵检测 (DID) 系统，它在学习和检测阶段使用流量的纯内容以及流量元数据。为此，我们采用了机器学习社区最近开发的深度学习技术。由于深度学习的固有特性，它可以处理高维数据内容，从而发现自动提取的交通特征之间的复杂关系。为了评估提议的 DID 系统，我们使用 ISCX IDS 2017 数据集。精确率和召回率等评估指标分别达到 0.992 美元和 0.998 美元，表明所提出的 DID 方法的高性能。我们采用了机器学习社区最近开发的深度学习技术。由于深度学习的固有特性，它可以处理高维数据内容，从而发现自动提取的交通特征之间的复杂关系。为了评估提议的 DID 系统，我们使用 ISCX IDS 2017 数据集。精确率和召回率等评估指标分别达到 0.992 美元和 0.998 美元，表明所提出的 DID 方法的高性能。我们采用了机器学习社区最近开发的深度学习技术。由于深度学习的固有特性，它可以处理高维数据内容，从而发现自动提取的交通特征之间的复杂关系。为了评估提议的 DID 系统，我们使用 ISCX IDS 2017 数据集。精确率和召回率等评估指标分别达到 0.992 美元和 0.998 美元，表明所提出的 DID 方法的高性能。