论文标题
在网络入侵检测系统中增强针对对抗性例子的鲁棒性
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems
论文作者
论文摘要
近年来，数量和品种的网络攻击增加都需要建立更复杂的网络入侵检测系统（NIDS）。当这些NID可以监视通过网络中的所有流量传播时，例如在软件定义的网络（SDN）上部署时，它们的表现更好。由于无法检测到零日攻击，传统上用于检测恶意流量的基于签名的NID开始被基于神经网络构建的基于异常的NID所取代。但是，最近已经证明，这样的NID有自己的缺点，即容易受到对抗性示例攻击的影响。此外，它们主要是在旧数据集中评估的，这些数据集并不代表网络系统可能面临的各种攻击。在本文中，我们将部分观察（repo）作为一种新机制进行了重建，以借助于能够在低较低的虚假警报设置中检测不同类型的网络攻击的自动编码器，并具有增强的鲁棒性针对对抗性示例攻击。我们在具有多种网络攻击的数据集上进行的评估表明，与其他最新提出的异常检测器相比，在正常情况下，在正常环境中可以将恶意流量的检测提高高达29％，在对抗环境中最多可提高45％。
The increase of cyber attacks in both the numbers and varieties in recent years demands to build a more sophisticated network intrusion detection system (NIDS). These NIDS perform better when they can monitor all the traffic traversing through the network like when being deployed on a Software-Defined Network (SDN). Because of the inability to detect zero-day attacks, signature-based NIDS which were traditionally used for detecting malicious traffic are beginning to get replaced by anomaly-based NIDS built on neural networks. However, recently it has been shown that such NIDS have their own drawback namely being vulnerable to the adversarial example attack. Moreover, they were mostly evaluated on the old datasets which don't represent the variety of attacks network systems might face these days. In this paper, we present Reconstruction from Partial Observation (RePO) as a new mechanism to build an NIDS with the help of denoising autoencoders capable of detecting different types of network attacks in a low false alert setting with an enhanced robustness against adversarial example attack. Our evaluation conducted on a dataset with a variety of network attacks shows denoising autoencoders can improve detection of malicious traffic by up to 29% in a normal setting and by up to 45% in an adversarial setting compared to other recently proposed anomaly detectors.
