DT-DS: CAN Intrusion Detection with Decision Tree Ensembles

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ACM Transactions on Cyber-Physical Systems Pub Date : 2023-01-21 DOI:10.1145/3566132
Jarul Mehta, Guillaume Richard, Loren Lugosch, Derek Yu, B. Meyer
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Abstract

The controller area network (CAN) protocol, used in many modern vehicles for real-time inter-device communications, is known to have cybersecurity vulnerabilities, putting passengers at risk for data exfiltration and control system sabotage. To address this issue, researchers have proposed to utilize security measures based on cryptography and message authentication; unfortunately, such approaches are often too computationally expensive to be deployed in real time on CAN devices. Additionally, they have developed machine learning (ML) techniques to detect anomalies in CAN traffic and thereby prevent attacks. The main disadvantage of existing ML-based techniques is that they either depend on additional computational hardware or they heuristically assume that all communication anomalies are malicious. In this article, we show that tree-based learning ensembles outperform anomaly-based techniques like AutoRegressive Integrated Moving Average (ARIMA) and Z-Score when used to detect attacks that result in increased bus utilization. We evaluated the detection capacity of three tree-based ensembles, Adaboost, gradient boosting, and random forests, and collectively refer to these as DT-DS. We conclude that the decision tree ensemble with Adaboost performs best with an area under curve (AUC) score of 0.999, closely followed by gradient boosting and random forests with 0.997 and 0.991 AUC scores, respectively, when trained using message profiles. We observe that with an increase in the observation window, the DT-DS models present an average AUC score of 0.999, and offer a nearly perfect detection of attacks, at the cost of increased latency in detection of attacked messages. We evaluate the performance of the IDS for Aeronautical Radio, Incorporated– (ARINC) encoded CAN communication traffic in avionic systems, generated using an aerospace testbench, ARINC-825TBv2. The IDS has been evaluated against the active attacks of a state-of-the-art predictive attacker model. Additionally, we observed that the performance of IDS approaches such as ARIMA and Z-Score degrade considerably with a decrease in the size of the observation time window. In contrast, the performance of DT-DS models is consistent, with only an average drop of 0.005 in the AUC score.
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DT-DS:基于决策树集成的CAN入侵检测
控制器区域网络(CAN)协议用于许多现代车辆的实时设备间通信,已知存在网络安全漏洞,使乘客面临数据泄露和控制系统破坏的风险。为了解决这个问题,研究人员提出了利用基于加密和消息认证的安全措施;不幸的是,这种方法通常在计算上过于昂贵,无法在CAN设备上实时部署。此外,他们还开发了机器学习(ML)技术来检测CAN流量中的异常情况,从而防止攻击。现有的基于机器学习的技术的主要缺点是,它们要么依赖于额外的计算硬件,要么启发式地假设所有通信异常都是恶意的。在本文中,我们表明,当用于检测导致总线利用率增加的攻击时,基于树的学习集成优于基于异常的技术,如自回归集成移动平均(ARIMA)和Z-Score。我们评估了三种基于树的系统,Adaboost,梯度增强和随机森林的检测能力,并将它们统称为DT-DS。我们得出结论,当使用消息概要进行训练时,Adaboost的决策树集成表现最佳,曲线下面积(AUC)得分为0.999,紧随其后的是梯度增强和随机森林,AUC得分分别为0.997和0.991。我们观察到,随着观察窗口的增加,DT-DS模型的平均AUC得分为0.999,并且提供了近乎完美的攻击检测,但代价是检测被攻击消息的延迟增加。我们评估了航空电子系统中航空无线电公司(ARINC)编码CAN通信流量的IDS的性能,使用ARINC- 825tbv2航空航天试验台生成。IDS已经针对最先进的预测攻击者模型的主动攻击进行了评估。此外,我们观察到,IDS方法(如ARIMA和Z-Score)的性能随着观测时间窗口大小的减小而显著下降。相比之下,DT-DS模型的表现较为一致,AUC得分平均仅下降0.005。
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
ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.70
自引率
4.30%
发文量
40
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