The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.
{"title":"Resilience of Multi-robot Systems to Physical Masquerade Attacks","authors":"Kacper Wardega, Roberto Tron, Wenchao Li","doi":"10.1109/SPW.2019.00031","DOIUrl":"https://doi.org/10.1109/SPW.2019.00031","url":null,"abstract":"The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115743774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Message from the DLS Organizers","authors":"","doi":"10.1109/spw.2019.00006","DOIUrl":"https://doi.org/10.1109/spw.2019.00006","url":null,"abstract":"","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"26 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126564502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Message from the SafeThings 2019 Organizers","authors":"","doi":"10.1109/spw.2019.00010","DOIUrl":"https://doi.org/10.1109/spw.2019.00010","url":null,"abstract":"","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"123 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116372075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Regulatory efforts such as the General Data Protection Regulation (GDPR) embody a notion of privacy risk that is centered around the fundamental rights of data subjects. This is, however, a fundamentally different notion of privacy risk than the one commonly used in threat modeling which is largely agnostic of involved data subjects. This mismatch hampers the applicability of privacy threat modeling approaches such as LINDDUN in a Data Protection by Design (DPbD) context. In this paper, we present a data subject-aware privacy risk assessment model in specific support of privacy threat modeling activities. This model allows the threat modeler to draw upon a more holistic understanding of privacy risk while assessing the relevance of specific privacy threats to the system under design. Additionally, we propose a number of improvements to privacy threat modeling, such as enriching Data Flow Diagram (DFD) system models with appropriate risk inputs (e.g., information on data types and involved data subjects). Incorporation of these risk inputs in DFDs, in combination with a risk estimation approach using Monte Carlo simulations, leads to a more comprehensive assessment of privacy risk. The proposed risk model has been integrated in threat modeling tool prototype and validated in the context of a realistic eHealth application.
{"title":"Privacy Risk Assessment for Data Subject-Aware Threat Modeling","authors":"Laurens Sion, D. Landuyt, Kim Wuyts, W. Joosen","doi":"10.1109/SPW.2019.00023","DOIUrl":"https://doi.org/10.1109/SPW.2019.00023","url":null,"abstract":"Regulatory efforts such as the General Data Protection Regulation (GDPR) embody a notion of privacy risk that is centered around the fundamental rights of data subjects. This is, however, a fundamentally different notion of privacy risk than the one commonly used in threat modeling which is largely agnostic of involved data subjects. This mismatch hampers the applicability of privacy threat modeling approaches such as LINDDUN in a Data Protection by Design (DPbD) context. In this paper, we present a data subject-aware privacy risk assessment model in specific support of privacy threat modeling activities. This model allows the threat modeler to draw upon a more holistic understanding of privacy risk while assessing the relevance of specific privacy threats to the system under design. Additionally, we propose a number of improvements to privacy threat modeling, such as enriching Data Flow Diagram (DFD) system models with appropriate risk inputs (e.g., information on data types and involved data subjects). Incorporation of these risk inputs in DFDs, in combination with a risk estimation approach using Monte Carlo simulations, leads to a more comprehensive assessment of privacy risk. The proposed risk model has been integrated in threat modeling tool prototype and validated in the context of a realistic eHealth application.","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116151695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.
{"title":"Devil in the Detail: Attack Scenarios in Industrial Applications","authors":"S. D. Antón, Alexander Hafner, H. Schotten","doi":"10.1109/SPW.2019.00040","DOIUrl":"https://doi.org/10.1109/SPW.2019.00040","url":null,"abstract":"In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123509662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weijia He, Jesse Martinez, Roshni Padhi, Lefan Zhang, Blase Ur
Household smart devices – internet-connected thermostats, lights, door locks, and more – have increased greatly in popularity. These devices provide convenience, yet can introduce issues related to safety, security, and usability. To better understand device owners' recent negative experiences with widely deployed smart devices and how those experiences impact the ability to provide a safe environment for users, we conducted an online, survey-based study of 72 participants who have smart devices in their own home. Participants reported struggling to diagnose and recover from power outages and network failures, misattributing some events to hacking. For devices featuring built-in learning, participants reported difficulty avoiding false alarms, communicating complex schedules, and resolving conflicting preferences. Finally, while many smart devices support end-user programming, participants reported fears of breaking the system by writing their own programs. To address these negative experiences, we propose a research agenda for improving the transparency of smart devices.
{"title":"When Smart Devices Are Stupid: Negative Experiences Using Home Smart Devices","authors":"Weijia He, Jesse Martinez, Roshni Padhi, Lefan Zhang, Blase Ur","doi":"10.1109/SPW.2019.00036","DOIUrl":"https://doi.org/10.1109/SPW.2019.00036","url":null,"abstract":"Household smart devices – internet-connected thermostats, lights, door locks, and more – have increased greatly in popularity. These devices provide convenience, yet can introduce issues related to safety, security, and usability. To better understand device owners' recent negative experiences with widely deployed smart devices and how those experiences impact the ability to provide a safe environment for users, we conducted an online, survey-based study of 72 participants who have smart devices in their own home. Participants reported struggling to diagnose and recover from power outages and network failures, misattributing some events to hacking. For devices featuring built-in learning, participants reported difficulty avoiding false alarms, communicating complex schedules, and resolving conflicting preferences. Finally, while many smart devices support end-user programming, participants reported fears of breaking the system by writing their own programs. To address these negative experiences, we propose a research agenda for improving the transparency of smart devices.","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"16 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120904942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern SCADA systems are interconnected with one or more industrial network protocols such as DNP3, Modbus/TCP, Ethernet/IP, and IEC 60870-5-104(IEC 104). IEC 104 is a particularly important protocol because it is one of the network protocols used for Automatic Generation Control (AGC), which is the algorithm that maintains electric power balance across large geographical areas. In this work, we focus on an empirical study and observation of a real-world, large scale IEC 104 power network.
{"title":"IEC 60870-5-104 Network Characterization of a Large-Scale Operational Power Grid","authors":"Kelvin Mai, Xi Qin, Neil Ortiz Silva, A. Cárdenas","doi":"10.1109/SPW.2019.00051","DOIUrl":"https://doi.org/10.1109/SPW.2019.00051","url":null,"abstract":"Modern SCADA systems are interconnected with one or more industrial network protocols such as DNP3, Modbus/TCP, Ethernet/IP, and IEC 60870-5-104(IEC 104). IEC 104 is a particularly important protocol because it is one of the network protocols used for Automatic Generation Control (AGC), which is the algorithm that maintains electric power balance across large geographical areas. In this work, we focus on an empirical study and observation of a real-world, large scale IEC 104 power network.","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123517434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Personal Voice Assistants (PVAs) such as the Siri, Amazon Echo and Google Home are now commonplace. PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. A user has little control over this process. She is unable to control the recording behaviour of surrounding PVAs, unable to signal her privacy requirements to back-end systems and unable to track conversation recordings. In this paper we explore techniques for embedding additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal when PVA activity is assumed. Any active PVA will embed this tag into their recorded audio stream. The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and where a recording was taken. We discuss different tagging techniques and application scenarios, and we describe the implementation of a prototype tagging device based on PocketSphinx. Using the popular PVA Google Home Mini we demonstrate that the device can tag conversations and that the tagging signal can be retrieved from conversations stored in the Google back-end system.
Siri、亚马逊Echo和谷歌Home等个人语音助手(pva)现在已经很常见了。pva持续监控可能被传输到云后端的会话，在云后端存储、处理甚至可能传递给其他服务提供商。用户对这个过程几乎没有控制权。她无法控制周围pva的记录行为，无法向后端系统发出她的隐私要求，也无法跟踪谈话记录。在本文中，我们探讨了在pva处理的声信号中嵌入附加信息的技术。当假定PVA活动时，用户使用一种发出声信号的标记装置。任何活跃的PVA都会将这个标签嵌入到录制的音频流中。标签可以向合作的PVA或后端系统发出信号，表明用户尚未给予录制同意。这个标签也可以用来追踪录音的时间和地点。我们讨论了不同的标记技术和应用场景，并描述了基于PocketSphinx的原型标记设备的实现。使用流行的PVA Google Home Mini，我们演示了该设备可以标记对话，并且可以从存储在Google后端系统中的对话中检索标记信号。
{"title":"Smart Speaker Privacy Control - Acoustic Tagging for Personal Voice Assistants","authors":"Peng Cheng, I. E. Bagci, Jeff Yan, U. Roedig","doi":"10.1109/SPW.2019.00035","DOIUrl":"https://doi.org/10.1109/SPW.2019.00035","url":null,"abstract":"Personal Voice Assistants (PVAs) such as the Siri, Amazon Echo and Google Home are now commonplace. PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. A user has little control over this process. She is unable to control the recording behaviour of surrounding PVAs, unable to signal her privacy requirements to back-end systems and unable to track conversation recordings. In this paper we explore techniques for embedding additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal when PVA activity is assumed. Any active PVA will embed this tag into their recorded audio stream. The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and where a recording was taken. We discuss different tagging techniques and application scenarios, and we describe the implementation of a prototype tagging device based on PocketSphinx. Using the popular PVA Google Home Mini we demonstrate that the device can tag conversations and that the tagging signal can be retrieved from conversations stored in the Google back-end system.","PeriodicalId":125351,"journal":{"name":"2019 IEEE Security and Privacy Workshops (SPW)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121102948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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