论文标题
物联网环境中入侵检测系统的新型降低方案
A Novel Dimension Reduction Scheme for Intrusion Detection Systems in IoT Environments
论文作者
论文摘要
物联网（IoT）为计算机网络的安全解决方案带来了新的挑战。到目前为止，入侵检测系统（IDS）是有效的安全工具之一，但是由异构协议和“事物”生成的大量数据与主机的约束资源一起，使一些当前的IDS方案被击败。为了授予IDSS在IoT环境中工作的能力，在本文中，我们提出了一种新的分布式减少尺寸方案，以应对有限的资源挑战。设计了一种新颖的自动编码器（AE），并学会了产生潜在空间。然后，受约束的主机/探针使用生成的权重以单个操作降低尺寸。压缩数据被传输到中央IDS服务器以验证流量类型。该方案旨在降低所需的带宽来通过压缩数据来传输数据，并减少主机中压缩任务的开销。在三个众所周知的网络流量数据集（UNSW-NB15，TON \ _IOT20和NSL-KDD）上评估了该方案，结果表明，我们可以拥有3维潜在空间（约90 \％压缩），而IDS检测准确性中没有任何明显的下降。
Internet of Things (IoT) brings new challenges to the security solutions of computer networks. So far, intrusion detection system (IDS) is one of the effective security tools, but the vast amount of data that is generated by heterogeneous protocols and "things" alongside the constrained resources of the hosts, make some of the present IDS schemes defeated. To grant IDSs the ability of working in the IoT environments, in this paper, we propose a new distributed dimension reduction scheme which addresses the limited resources challenge. A novel autoencoder (AE) designed, and it learns to generate a latent space. Then, the constrained hosts/probes use the generated weights to lower the dimension with a single operation. The compressed data is transferred to a central IDS server to verify the traffic type. This scheme aims to lower the needed bandwidth to transfer data by compressing it and also reduce the overhead of the compression task in the hosts. The proposed scheme is evaluated on three well-known network traffic datasets (UNSW-NB15, TON\_IoT20 and NSL-KDD), and the results show that we can have a 3-dimensional latent space (about 90\% compression) without any remarkable fall in IDS detection accuracy.
