Data are a main resource of a computer system, which can be transmitted over network from source to destination. While transmitting, it faces lot of security issues such as virus, malware, infection, error, and data loss. The security issues are the attacks that have to be detected and eliminated in efficient way to guarantee the secure transmission. The attack detection rates of existing Intrusion Detection Systems (IDS) are low, because the number of unknown attacks are high when compared to the known attacks in the network. Thus, recent researchers focus more on evaluation of known attacks attributes, that will help in identification of the attacks. But the difficulty here is the nature of the IDS datasets. The difficulty in any IDS dataset is to, too many attributes, irrelevant and unstructured in nature. So analyzing such attributes leads to a time consuming process and that produces an inefficient result. This article presents a combined approach Principle Component Analysis and Deep learning (PCA-DL) model to address above issues. The proposed PCA-DL method has achieved the accuracy 92.6% on detecting the attacks correctly.

中文翻译：

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使用主成分分析和深度学习模型的入侵检测系统中的经验模型

数据是计算机系统的主要资源，可以通过网络从源到目的地传输。在传输过程中，面临病毒、恶意软件、感染、错误、数据丢失等诸多安全问题。安全问题是必须以有效的方式检测和消除攻击以保证安全传输。现有入侵检测系统（IDS）的攻击检测率较低，因为与网络中的已知攻击相比，未知攻击的数量较多。因此，最近的研究人员更多地关注对已知攻击属性的评估，这将有助于识别攻击。但这里的困难在于 IDS 数据集的性质。任何 IDS 数据集的困难在于，太多的属性本质上是不相关和非结构化的。因此，分析这些属性会导致一个耗时的过程并产生低效的结果。本文提出了一种组合方法主成分分析和深度学习 (PCA-DL) 模型来解决上述问题。所提出的 PCA-DL 方法在正确检测攻击方面达到了 92.6% 的准确率。