{"title":"CANSat-IDS:基于 CAN 流量组合分类的卫星自适应分布式入侵检测系统","authors":"","doi":"10.1016/j.cose.2024.104033","DOIUrl":null,"url":null,"abstract":"<div><p>The increasing dependence on satellite technology for critical applications, such as telecommunications, Earth observation, and navigation, underscores the need for robust security measures to safeguard these assets from potential cyber threats. Moreover, as many satellite systems rely on the Controller Area Network (CAN) protocol for efficient data exchange among onboard subsystems, they become prime targets for cyberattacks. While contributions present various options for detecting attacks in the CAN bus, no one proposes an architecture suitable for satellite systems. To address this concern, this paper presents a novel approach to develop an adaptive distributed Intrusion Detection System (IDS) for satellites, which integrates machine and deep learning techniques for the classification of CAN frames. This system is specifically designed to overcome the inherent power and computational challenges of satellite operations by executing time-based anomaly detection on board, and content-based detection at the ground segment. To evaluate the effectiveness of the proposed solution, experiments are conducted using representative Datasets. The obtained results demonstrate that the distributed IDS presented in this research offers a promising solution to improve the security of satellite systems by achieving high detection rates ranging from 91.12% to 99.86% (F1-score).</p></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CANSat-IDS: An adaptive distributed Intrusion Detection System for satellites, based on combined classification of CAN traffic\",\"authors\":\"\",\"doi\":\"10.1016/j.cose.2024.104033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The increasing dependence on satellite technology for critical applications, such as telecommunications, Earth observation, and navigation, underscores the need for robust security measures to safeguard these assets from potential cyber threats. Moreover, as many satellite systems rely on the Controller Area Network (CAN) protocol for efficient data exchange among onboard subsystems, they become prime targets for cyberattacks. While contributions present various options for detecting attacks in the CAN bus, no one proposes an architecture suitable for satellite systems. To address this concern, this paper presents a novel approach to develop an adaptive distributed Intrusion Detection System (IDS) for satellites, which integrates machine and deep learning techniques for the classification of CAN frames. This system is specifically designed to overcome the inherent power and computational challenges of satellite operations by executing time-based anomaly detection on board, and content-based detection at the ground segment. To evaluate the effectiveness of the proposed solution, experiments are conducted using representative Datasets. The obtained results demonstrate that the distributed IDS presented in this research offers a promising solution to improve the security of satellite systems by achieving high detection rates ranging from 91.12% to 99.86% (F1-score).</p></div>\",\"PeriodicalId\":51004,\"journal\":{\"name\":\"Computers & Security\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Security\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167404824003389\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Security","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167404824003389","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
电信、地球观测和导航等关键应用越来越依赖卫星技术,这凸显了采取强有力的安全措施保护这些资产免受潜在网络威胁的必要性。此外,由于许多卫星系统依赖控制器局域网(CAN)协议在星载子系统之间进行高效的数据交换,因此它们成为网络攻击的主要目标。虽然已有论文提出了各种检测 CAN 总线攻击的方案,但没有人提出适合卫星系统的架构。为了解决这一问题,本文提出了一种新方法,为卫星开发自适应分布式入侵检测系统(IDS),该系统集成了机器学习和深度学习技术,用于 CAN 帧的分类。该系统通过在卫星上执行基于时间的异常检测和在地面段执行基于内容的检测,克服了卫星运行固有的功率和计算挑战。为评估所提解决方案的有效性,我们使用具有代表性的数据集进行了实验。实验结果表明,本研究提出的分布式 IDS 能够实现 91.12% 到 99.86% 的高检测率(F1-分数),为提高卫星系统的安全性提供了一个前景广阔的解决方案。
CANSat-IDS: An adaptive distributed Intrusion Detection System for satellites, based on combined classification of CAN traffic
The increasing dependence on satellite technology for critical applications, such as telecommunications, Earth observation, and navigation, underscores the need for robust security measures to safeguard these assets from potential cyber threats. Moreover, as many satellite systems rely on the Controller Area Network (CAN) protocol for efficient data exchange among onboard subsystems, they become prime targets for cyberattacks. While contributions present various options for detecting attacks in the CAN bus, no one proposes an architecture suitable for satellite systems. To address this concern, this paper presents a novel approach to develop an adaptive distributed Intrusion Detection System (IDS) for satellites, which integrates machine and deep learning techniques for the classification of CAN frames. This system is specifically designed to overcome the inherent power and computational challenges of satellite operations by executing time-based anomaly detection on board, and content-based detection at the ground segment. To evaluate the effectiveness of the proposed solution, experiments are conducted using representative Datasets. The obtained results demonstrate that the distributed IDS presented in this research offers a promising solution to improve the security of satellite systems by achieving high detection rates ranging from 91.12% to 99.86% (F1-score).
期刊介绍:
Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world.
Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.