In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Gianmarco Baldini
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引用次数: 0

Abstract

Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security networks based on the concept of multi-scale histograms, which capture the frequencies of message identifiers in CAN-bus in-vehicle networks. In comparison to existing approaches in the literature based on a single histogram, the proposed approach widens the informative context used by the IDS for traffic analysis by taking into consideration sequences of two and three CAN-bus messages to create multi-scale dictionaries. The histograms are created from windows of in-vehicle network traffic. A preliminary multi-scale histogram model is created using only legitimate traffic. Against this model, the IDS performs traffic analysis to create a feature space based on the correlation of the histograms. Then, the created feature space is given in input to a Convolutional Neural Network (CNN) for the identification of the windows of traffic where the attack is present. The proposed approach has been evaluated on two different public data sets achieving a very competitive performance in comparison to the literature.
基于卷积神经网络和多尺度直方图的车载网络入侵检测系统
近年来,现代车辆的网络安全问题越来越受到研究界的关注。入侵检测系统(ids)是一种用于检测和减轻网络安全风险的技术。本文提出了一种基于多尺度直方图的车载安全网络入侵检测系统的实现方法,该方法可以捕获can总线车载网络中消息标识符的频率。与现有文献中基于单一直方图的方法相比,本文提出的方法通过考虑两个和三个can总线消息序列来创建多尺度字典,从而扩大了IDS用于流量分析的信息上下文。直方图是从车载网络流量窗口创建的。仅使用合法流量创建初步的多尺度直方图模型。针对该模型,IDS执行流量分析,根据直方图的相关性创建特征空间。然后,将创建的特征空间作为输入输入给卷积神经网络(CNN),用于识别存在攻击的流量窗口。所提出的方法已经在两个不同的公共数据集上进行了评估,与文献相比,实现了非常有竞争力的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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