Fingerprinting of quantum computer devices is a new threat that poses a challenge to shared, cloud-based quantum computers. Fingerprinting can allow adversaries to map quantum computer infras-tructures, uniquely identify cloud-based devices which otherwise have no public identifiers, and it can assist other adversarial attacks. This work shows idle tomography-based fingerprinting method based on crosstalk-induced errors in NISQ quantum computers. The device- and locality-specific fingerprinting results show prediction accuracy values of 99 . 1% and 95 . 3%, respectively.
{"title":"Short Paper: Device- and Locality-Specific Fingerprinting of Shared NISQ Quantum Computers","authors":"Mi Allen, Deng Shuwen, Szefer Jakub","doi":"10.1145/3505253.3505261","DOIUrl":"https://doi.org/10.1145/3505253.3505261","url":null,"abstract":"Fingerprinting of quantum computer devices is a new threat that poses a challenge to shared, cloud-based quantum computers. Fingerprinting can allow adversaries to map quantum computer infras-tructures, uniquely identify cloud-based devices which otherwise have no public identifiers, and it can assist other adversarial attacks. This work shows idle tomography-based fingerprinting method based on crosstalk-induced errors in NISQ quantum computers. The device- and locality-specific fingerprinting results show prediction accuracy values of 99 . 1% and 95 . 3%, respectively.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"421 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115612121","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}
Hawzhin Mohammed, Faiq Khalid, P. Sawyer, Gabriella V. Cataloni, S. R. Hasan
Modern Resource-Constrained (RC) Internet of Things (IoT) devices are subject to several types of attacks, including hardware-level attacks. Most of the existing state-of-the-art solutions are invasive, require expensive design time interventions, or need dataset generation from non-trusted RC-IoT devices or both. We argue that the health of modern RC-IoT devices requires a final line of defense against possible hardware attacks that go undetected during the IC design and test process. Hence, in this paper, we propose a defense methodology against non-zero-day and zero-day attacks, leveraging machine learning techniques trained on the dataset obtained without design time intervention and using ‘only’ trusted IoT devices. In the process, a complete eco-system is developed where data is generated through a trusted group of devices, and machine learning is done on these trusted datasets. Next, this trusted trained model is deployed in regular IoT systems that contain untrusted devices, where the attack on untrusted devices can be detected in real-time. Our results indicate that for non-zero-day attacks, the proposed technique can concurrently detect DoS and power depletion attacks with an accuracy of about 80%. Similarly, zero-day attack experiments are able to detect the attack without fail as well.
{"title":"InTrust-IoT: Intelligent Ecosystem based on Power Profiling of Trusted device(s) in IoT for Hardware Trojan Detection","authors":"Hawzhin Mohammed, Faiq Khalid, P. Sawyer, Gabriella V. Cataloni, S. R. Hasan","doi":"10.1145/3505253.3505262","DOIUrl":"https://doi.org/10.1145/3505253.3505262","url":null,"abstract":"Modern Resource-Constrained (RC) Internet of Things (IoT) devices are subject to several types of attacks, including hardware-level attacks. Most of the existing state-of-the-art solutions are invasive, require expensive design time interventions, or need dataset generation from non-trusted RC-IoT devices or both. We argue that the health of modern RC-IoT devices requires a final line of defense against possible hardware attacks that go undetected during the IC design and test process. Hence, in this paper, we propose a defense methodology against non-zero-day and zero-day attacks, leveraging machine learning techniques trained on the dataset obtained without design time intervention and using ‘only’ trusted IoT devices. In the process, a complete eco-system is developed where data is generated through a trusted group of devices, and machine learning is done on these trusted datasets. Next, this trusted trained model is deployed in regular IoT systems that contain untrusted devices, where the attack on untrusted devices can be detected in real-time. Our results indicate that for non-zero-day attacks, the proposed technique can concurrently detect DoS and power depletion attacks with an accuracy of about 80%. Similarly, zero-day attack experiments are able to detect the attack without fail as well.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122859940","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}
As finite state machines (FSMs) control the behavior of sequential circuits, they can be a target for attacks. With laser-based fault injection (LFI), an adversary may attain unauthorized access to sensitive states by altering the values of individual state flip-flops (FFs). Although standard error correction/detection techniques improve FSM resiliency, all states and FFs of an FSM are assumed equally critical to protect, incurring significant overhead. In this paper, we introduce a novel spatial vulnerability metric to aid the security analysis, which precisely manifests the susceptibility of FSM designs to LFI based on state FF sensitivity and placement. A novel encoding and spatially aware physical design framework (SPARSE) are then proposed that co-optimize the FSM encoding and state FF placement to minimize LFI susceptibility. SPARSE’s encoding uses the minimum number of FFs by placing security-sensitive FFs a sufficient distance apart from other FFs. SPARSE is demonstrated on 5 benchmarks using commercial CAD tools and outperforms other FSM encoding schemes in terms of security, area, and PDP.
{"title":"SPARSE: Spatially Aware LFI Resilient State Machine Encoding","authors":"Muhtadi Choudhury, Shahin Tajik, Domenic Forte","doi":"10.1145/3505253.3505254","DOIUrl":"https://doi.org/10.1145/3505253.3505254","url":null,"abstract":"As finite state machines (FSMs) control the behavior of sequential circuits, they can be a target for attacks. With laser-based fault injection (LFI), an adversary may attain unauthorized access to sensitive states by altering the values of individual state flip-flops (FFs). Although standard error correction/detection techniques improve FSM resiliency, all states and FFs of an FSM are assumed equally critical to protect, incurring significant overhead. In this paper, we introduce a novel spatial vulnerability metric to aid the security analysis, which precisely manifests the susceptibility of FSM designs to LFI based on state FF sensitivity and placement. A novel encoding and spatially aware physical design framework (SPARSE) are then proposed that co-optimize the FSM encoding and state FF placement to minimize LFI susceptibility. SPARSE’s encoding uses the minimum number of FFs by placing security-sensitive FFs a sufficient distance apart from other FFs. SPARSE is demonstrated on 5 benchmarks using commercial CAD tools and outperforms other FSM encoding schemes in terms of security, area, and PDP.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133826695","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}
Rabimba Karanjai, Lei Xu, Lin Chen, Fengwei Zhang, Zhimin Gao, W. Shi
Modern computer systems tend to rely on large trusted computing bases (TCBs) for operations. To address the TCB bloating problem, hardware vendors have developed mechanisms to enable or facilitate the creation of a trusted execution environment (TEE) in which critical software applications can execute securely in an isolated environment. Even under the circumstance that a host OS is compromised by an adversary, key security properties such as confidentiality and integrity of the software inside the TEEs can be guaranteed. The promise of integrity and security has driven developers to adopt it for use cases involving access control, PKS, IoT among other things. Among these applications include blockchain-related use cases. The usage of the TEEs doesn’t come without its own implementation challenges and potential pitfalls. In this paper, we examine the assumptions, security models, and operational environments of the proposed TEE use cases of blockchain-based applications. The exercise and analysis help the hardware TEE research community to identify some open challenges and opportunities for research and rethink the design of hardware TEEs in general.
{"title":"Lessons Learned from Blockchain Applications of Trusted Execution Environments and Implications for Future Research","authors":"Rabimba Karanjai, Lei Xu, Lin Chen, Fengwei Zhang, Zhimin Gao, W. Shi","doi":"10.1145/3505253.3505259","DOIUrl":"https://doi.org/10.1145/3505253.3505259","url":null,"abstract":"Modern computer systems tend to rely on large trusted computing bases (TCBs) for operations. To address the TCB bloating problem, hardware vendors have developed mechanisms to enable or facilitate the creation of a trusted execution environment (TEE) in which critical software applications can execute securely in an isolated environment. Even under the circumstance that a host OS is compromised by an adversary, key security properties such as confidentiality and integrity of the software inside the TEEs can be guaranteed. The promise of integrity and security has driven developers to adopt it for use cases involving access control, PKS, IoT among other things. Among these applications include blockchain-related use cases. The usage of the TEEs doesn’t come without its own implementation challenges and potential pitfalls. In this paper, we examine the assumptions, security models, and operational environments of the proposed TEE use cases of blockchain-based applications. The exercise and analysis help the hardware TEE research community to identify some open challenges and opportunities for research and rethink the design of hardware TEEs in general.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114850480","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}
A. Suresh, Abdullah Ash-Saki, Mahababul Alam, R. Topaloglu, Swaroop Ghosh
In the Noisy Intermediate-Scale Quantum (NISQ) realm, efficient quantum circuit compilation is critical to ensure successful computation. Several third-party compilers are improving the compilation times and depth/gate counts. Untrusted third parties or a particular version of a trusted compiler may allow an attacker to steal, clone, and/or reverse engineer the quantum circuit. We propose to obfuscate quantum circuits by employing dummy CNOT gates to prevent such threats. If the adversary clones the obfuscated design, he/she will get faulty results. We propose a metric-based dummy gate insertion process to ensure maximum corruption of functionality measured using Total Variation Distance (TVD) and validated using IBM’s noisy simulators. Our metric guided dummy gate insertion process achieves TVD of up to 28.83%, and performs 10.14% better than the average TVD and performs within 12.45% of the best obtainable TVD for the test benchmarks. The removal of dummy gates by the designer post-compilation to restore functionality as well as other finer details have been addressed.
{"title":"Short Paper: A Quantum Circuit Obfuscation Methodology for Security and Privacy","authors":"A. Suresh, Abdullah Ash-Saki, Mahababul Alam, R. Topaloglu, Swaroop Ghosh","doi":"10.1145/3505253.3505260","DOIUrl":"https://doi.org/10.1145/3505253.3505260","url":null,"abstract":"In the Noisy Intermediate-Scale Quantum (NISQ) realm, efficient quantum circuit compilation is critical to ensure successful computation. Several third-party compilers are improving the compilation times and depth/gate counts. Untrusted third parties or a particular version of a trusted compiler may allow an attacker to steal, clone, and/or reverse engineer the quantum circuit. We propose to obfuscate quantum circuits by employing dummy CNOT gates to prevent such threats. If the adversary clones the obfuscated design, he/she will get faulty results. We propose a metric-based dummy gate insertion process to ensure maximum corruption of functionality measured using Total Variation Distance (TVD) and validated using IBM’s noisy simulators. Our metric guided dummy gate insertion process achieves TVD of up to 28.83%, and performs 10.14% better than the average TVD and performs within 12.45% of the best obtainable TVD for the test benchmarks. The removal of dummy gates by the designer post-compilation to restore functionality as well as other finer details have been addressed.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122539444","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}
Trusted Execution Environment (TEE) is a popular technology to protect sensitive data and programs. Recent TEEs have proposed the concept of enclaves to execute code processing sensitive data, which cannot be tampered with even by a malicious OS. However, due to hardware limitations and security requirements, existing TEE architectures usually offer limited memory management, such as dynamic memory allocation, defragmentation, etc. In this paper, we present Ashman—a novel software-based memory management extension of TEE on RISC-V, including dynamic memory allocation, migration, and defragmentation. We integrate Ashman into a self-designed TEE and evaluate the performance on a real-world development board. Experimental results have shown that Ashman provides memory management functions similar to native user applications while ensuring enclave security without modifying hardware.
{"title":"A Novel Memory Management for RISC-V Enclaves","authors":"Hao Li, Weijie Huang, Mingde Ren, Hongyi Lu, Zhenyu Ning, Heming Cui, Fengwei Zhang","doi":"10.1145/3505253.3505257","DOIUrl":"https://doi.org/10.1145/3505253.3505257","url":null,"abstract":"Trusted Execution Environment (TEE) is a popular technology to protect sensitive data and programs. Recent TEEs have proposed the concept of enclaves to execute code processing sensitive data, which cannot be tampered with even by a malicious OS. However, due to hardware limitations and security requirements, existing TEE architectures usually offer limited memory management, such as dynamic memory allocation, defragmentation, etc. In this paper, we present Ashman—a novel software-based memory management extension of TEE on RISC-V, including dynamic memory allocation, migration, and defragmentation. We integrate Ashman into a self-designed TEE and evaluate the performance on a real-world development board. Experimental results have shown that Ashman provides memory management functions similar to native user applications while ensuring enclave security without modifying hardware.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115184211","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}
Trustworthy sensor data is important for IoT sensing systems. As such, these systems need to guarantee that the sensor data is acquired by the correct device and has not been tampered with. However, IoT sensing systems can be quite complex and are often composed of multiple components, i.e., a main device and subordinate sensors. The main device is responsible for gathering and processing the data from the subordinate sensor and reports the result to a server. In order to guarantee data correctness, we introduce two types of physically unclonable function (PUF): one for the main device and one for the subordinate sensor. The main device has a trusted execution environment (TEE) for critical processing, and the correctness of the TEE is guaranteed by remote attestation based on a PUF. The subordinate sensor sends the sensor data to the main device with a message authentication code (MAC) based on a PUF. We implemented a trusted IoT sensing system using a RISC-V Keystone with a PRINCE Glitch PUF for the main device and a Raspberry Pi that simulates a CMOS image sensor PUF for the subordinate sensor.
{"title":"Towards Trusted IoT Sensing Systems: Implementing PUF as Secure Key Generator for Root of Trust and Message Authentication Code","authors":"Kota Yoshida, K. Suzaki, T. Fujino","doi":"10.1145/3505253.3505258","DOIUrl":"https://doi.org/10.1145/3505253.3505258","url":null,"abstract":"Trustworthy sensor data is important for IoT sensing systems. As such, these systems need to guarantee that the sensor data is acquired by the correct device and has not been tampered with. However, IoT sensing systems can be quite complex and are often composed of multiple components, i.e., a main device and subordinate sensors. The main device is responsible for gathering and processing the data from the subordinate sensor and reports the result to a server. In order to guarantee data correctness, we introduce two types of physically unclonable function (PUF): one for the main device and one for the subordinate sensor. The main device has a trusted execution environment (TEE) for critical processing, and the correctness of the TEE is guaranteed by remote attestation based on a PUF. The subordinate sensor sends the sensor data to the main device with a message authentication code (MAC) based on a PUF. We implemented a trusted IoT sensing system using a RISC-V Keystone with a PRINCE Glitch PUF for the main device and a Raspberry Pi that simulates a CMOS image sensor PUF for the subordinate sensor.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114568606","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}
We present a new and practical framework for security verification of secure architectures. Specifically, we break the verification task into external verification and internal verification. External verification considers the external protocols, i.e. interactions between users, compute servers, network entities, etc. Meanwhile, internal verification considers the interactions between hardware and software components within each server. This verification framework is general-purpose and can be applied to a stand-alone server, or a large-scale distributed system. We evaluate our verification method on the CloudMonatt and HyperWall architectures as examples.
{"title":"Practical and Scalable Security Verification of Secure Architectures","authors":"Jakub Szefer, Tianwei Zhang, R. Lee","doi":"10.1145/3505253.3505256","DOIUrl":"https://doi.org/10.1145/3505253.3505256","url":null,"abstract":"We present a new and practical framework for security verification of secure architectures. Specifically, we break the verification task into external verification and internal verification. External verification considers the external protocols, i.e. interactions between users, compute servers, network entities, etc. Meanwhile, internal verification considers the interactions between hardware and software components within each server. This verification framework is general-purpose and can be applied to a stand-alone server, or a large-scale distributed system. We evaluate our verification method on the CloudMonatt and HyperWall architectures as examples.","PeriodicalId":342645,"journal":{"name":"Workshop on Hardware and Architectural Support for Security and Privacy","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126046787","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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