With the increasing dependency of daily life over computer networks, the importance of these networks security becomes prominent. Different intrusion attacks to networks have been designed and the attackers are working on improving them. Thus the ability to detect intrusion with limited number of labeled data is desirable to provide networks with higher level of security. In this paper we design an intrusion detection system based on a deep neural network. The proposed system is based on self-supervised contrastive learning where a huge amount of unlabeled data can be used to generate informative representation suitable for various downstream tasks with limited number of labeled data. Using different experiments, we have shown that the proposed system presents an accuracy of 94.05% over the UNSW-NB15 dataset, an improvement of 4.22% in comparison to previous method based on self-supervised learning. Our simulations have also shown impressive results when the size of labeled training data is limited. The performance of the resulting Encoder Block trained on UNSW-NB15 dataset has also been tested on other datasets for representation extraction which shows competitive results in downstream tasks.

翻译：随着日常生活对计算机网络的依赖性日益增加，这些网络的安全重要性日益凸显。当前已设计出多种针对网络的入侵攻击方法，且攻击者正持续改进这些技术。因此，在标记数据有限的情况下检测入侵的能力对于提供更高级别的网络安全至关重要。本文设计了一种基于深度神经网络的入侵检测系统。该系统采用自监督对比学习方法，能够利用海量未标记数据生成适用于多种下游任务的表征信息，且仅需少量标记数据。通过不同实验表明，该系统在UNSW-NB15数据集上的准确率达到94.05%，相较于先前基于自监督学习的方法提升了4.22%。模拟实验还显示，当标记训练数据规模受限时，本方法依然取得了显著成果。基于UNSW-NB15数据集训练的编码器模块在其他数据集的表征提取任务中同样表现出竞争力，并在下游任务中获得了具有可比性的结果。