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DualNet: Locate Then Detect Effective Payload with Deep Attention Network

机译：Dualnet：找到具有深受关注网络的有效有效载荷

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摘要

Network intrusion detection (NID) is an essential defense strategy that is used to discover the trace of suspicious user behaviour in large-scale cyberspace, and machine learning (ML), due to its capability of automation and intelligence, has been gradually adopted as a mainstream hunting method in recent years. However, traditional ML based network intrusion detection systems (NIDSs) are not effective to recognize unknown threats and their high detection rate often comes with the cost of high false alarms, which leads to the problem of alarm fatigue. To address the above problems, in this paper, we propose a novel neural network based detection system, DualNet, which is constructed with a general feature extraction stage and a crucial feature learning stage. DualNet can rapidly reuse the spatial-temporal features in accordance with their importance to facilitate the entire learning process and simultaneously mitigate several optimization problems occurred in deep learning (DL). We evaluate the DualNet on two benchmark cyber attack datasets, NSL-KDD and UNSW-NB15. Our experiment shows that DualNet outperforms classical ML based NIDSs and is more effective than existing DL methods for NID in terms of accuracy, detection rate and false alarm rate.
机译：网络入侵检测（NID）是一种基本防御策略，用于发现大规模网络空间中的可疑用户行为以及机器学习（ML），由于其自​​动化和智能能力，已逐渐被采用近年来主流狩猎方法。然而，基于传统的基于ML的网络入侵检测系统（NIDS）无效地识别未知威胁，并且它们的高检测率通常具有高误报的成本，这导致警报疲劳问题。为了解决上述问题，在本文中，我们提出了一种新型神经网络的基于神经网络的检测系统，这是用一般特征提取阶段和关键特征学习阶段构造的双网络。 Dualnet可以根据重要性快速重复使用空间时间特征，以便于整个学习过程，同时减轻深度学习（DL）中发生的几个优化问题。我们在两个基准网络攻击数据集，NSL-KDD和UNSW-NB15上评估Dualnet。我们的实验表明，Dualnet优于基于古典ML的NIDS，并且比在准确度，检测率和误报率方面都比现有的DL方法更有效。

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