RIDS

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ACM Transactions on Cyber-Physical Systems Pub Date : 2021-04-01 DOI:10.1145/3441458
Pierre-François Gimenez, Jonathan Roux, E. Alata, G. Auriol, M. Kaâniche, V. Nicomette
{"title":"RIDS","authors":"Pierre-François Gimenez, Jonathan Roux, E. Alata, G. Auriol, M. Kaâniche, V. Nicomette","doi":"10.1145/3441458","DOIUrl":null,"url":null,"abstract":"The expansion of the Internet-of-Things (IoT) market is visible in homes, factories, public places, and smart cities. While the massive deployment of connected devices offers opportunities to improve quality of life and to develop new services, the impact of such devices on the security of the users in a context where the level of malicious threat continues to increase is a major concern. One of the challenges is the heterogeneity and constant evolution of wireless technologies and protocols used. To overcome this problem, we propose RIDS, a Radio Intrusion Detection System that is based on the monitoring and profiling of radio communications at the physical layer level using autoencoder neural networks. RIDS is independent of the wireless protocols and modulation technologies used. Besides, it is designed to provide a threefold diagnosis of the detected anomalies: temporal (start and end date of the detected anomaly), frequential (main frequency of the anomaly), and spatial (location of the origin of the anomaly). To demonstrate the relevance and the efficiency of our approach, we collected a large dataset of radio-communications recorded with three different probes deployed in an experimental room. Multiple real-world attacks involving a wide variety of communication technologies are also injected to assess the detection and diagnosis efficiency. The results demonstrate the efficiency of RIDS in detecting and diagnosing anomalies that occurred in the 400–500 Mhz and 800–900 Mhz frequency bands. It is noteworthy that compromised devices and attacks using these communication bands are generally not easily covered by traditional solutions.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Cyber-Physical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3441458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 2

Abstract

The expansion of the Internet-of-Things (IoT) market is visible in homes, factories, public places, and smart cities. While the massive deployment of connected devices offers opportunities to improve quality of life and to develop new services, the impact of such devices on the security of the users in a context where the level of malicious threat continues to increase is a major concern. One of the challenges is the heterogeneity and constant evolution of wireless technologies and protocols used. To overcome this problem, we propose RIDS, a Radio Intrusion Detection System that is based on the monitoring and profiling of radio communications at the physical layer level using autoencoder neural networks. RIDS is independent of the wireless protocols and modulation technologies used. Besides, it is designed to provide a threefold diagnosis of the detected anomalies: temporal (start and end date of the detected anomaly), frequential (main frequency of the anomaly), and spatial (location of the origin of the anomaly). To demonstrate the relevance and the efficiency of our approach, we collected a large dataset of radio-communications recorded with three different probes deployed in an experimental room. Multiple real-world attacks involving a wide variety of communication technologies are also injected to assess the detection and diagnosis efficiency. The results demonstrate the efficiency of RIDS in detecting and diagnosing anomalies that occurred in the 400–500 Mhz and 800–900 Mhz frequency bands. It is noteworthy that compromised devices and attacks using these communication bands are generally not easily covered by traditional solutions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
在家庭、工厂、公共场所、智慧城市等领域,物联网(IoT)市场正在不断扩大。虽然联网设备的大规模部署为提高生活质量和开发新服务提供了机会,但在恶意威胁水平持续增加的背景下,此类设备对用户安全的影响是一个主要问题。其中一个挑战是所使用的无线技术和协议的异构性和不断发展。为了克服这个问题,我们提出了RIDS,一个无线电入侵检测系统,它基于使用自编码器神经网络在物理层监测和分析无线电通信。rid独立于所使用的无线协议和调制技术。此外,它还提供了对检测到的异常的三重诊断:时间(检测到异常的开始和结束日期)、频率(异常的主频率)和空间(异常的起源位置)。为了证明我们的方法的相关性和效率,我们收集了一个大型的无线电通信数据集,记录了在一个实验室内部署的三种不同的探针。还注入了涉及多种通信技术的多种真实攻击,以评估检测和诊断效率。结果表明,RIDS在检测和诊断400-500 Mhz和800-900 Mhz频段的异常方面是有效的。值得注意的是,使用这些通信频段的受损设备和攻击通常不容易被传统解决方案覆盖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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
期刊最新文献
A Comprehensive Threat Modelling Analysis for Distributed Energy Resources Carving out Control Code: Automated Identification of Control Software in Autopilot Systems Cooperative Driving of Connected Autonomous Vehicles using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning On Cyber-Physical Fault Resilience in Data Communication: A Case From A LoRaWAN Network Systems Design
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1