Pub Date : 2024-02-05DOI: 10.1109/LES.2024.3360615
Mona Hashemi;Siamak Mohammadi;Trevor E. Carlson
The involvement of external parties in integrated circuit (IC) supply chain has raised a number of security issues, such as the use of device cloning, overproduction, and unauthorized integration/activation. One potential solution to this problem, logic locking, restricts access to the hardware unless the correct key is provided. Existing locking methods target limited attacks and show scalability issues. In this letter we presents LOTUS, a scalable and multilayered locking framework that provides a solution for multimodule designs by employing pseudo-dynamic keys. An important aspect of this work is that it triggers an irreversible failure once an incorrect key is applied. This evaluation demonstrates this letter’s resiliency against various deobfuscation attacks like KC2, AppSAT, OMLA, SAIL, and SCOPE with low overhead. Due to its scalability, low overhead, and destructive-when-wrong structure, LOTUS is a practical solution for large, complex, and safety-critical designs.
{"title":"LOTUS: A Scalable Framework to Lock Multimodule Designs With One-Time Self-Destructing Key","authors":"Mona Hashemi;Siamak Mohammadi;Trevor E. Carlson","doi":"10.1109/LES.2024.3360615","DOIUrl":"10.1109/LES.2024.3360615","url":null,"abstract":"The involvement of external parties in integrated circuit (IC) supply chain has raised a number of security issues, such as the use of device cloning, overproduction, and unauthorized integration/activation. One potential solution to this problem, logic locking, restricts access to the hardware unless the correct key is provided. Existing locking methods target limited attacks and show scalability issues. In this letter we presents LOTUS, a scalable and multilayered locking framework that provides a solution for multimodule designs by employing pseudo-dynamic keys. An important aspect of this work is that it triggers an irreversible failure once an incorrect key is applied. This evaluation demonstrates this letter’s resiliency against various deobfuscation attacks like KC2, AppSAT, OMLA, SAIL, and SCOPE with low overhead. Due to its scalability, low overhead, and destructive-when-wrong structure, LOTUS is a practical solution for large, complex, and safety-critical designs.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"413-416"},"PeriodicalIF":1.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the inevitable adoption of zero trust architectures (ZTAs) for enterprise networks, there is a need to continuously gauge the security health of connected devices. This requires runtime monitoring of the devices in the network. The challenge, especially in resource-constrained environments, is to ensure trusted monitoring at a fine granularity. In this letter, we propose ProMiSE, a framework that overcomes this challenge and provides an online non-tamperable metric called trust score to quantify the security health of devices in a ZTA network. We use real-time hardware tracking of microarchitectural signals in the CPU to compute the trust score in a security co-processor that is isolated from the device’s computing stack. The trust score for each device is sent to the ZTA host for corresponding responses. We evaluate ProMiSE on an open-source RISC-V processor with different threat vectors, including ransomware, return-oriented programming (RoP) attacks, and cache-based microarchitectural attacks. We also deploy the framework on an AMD Artix 7AC701 FPGA and present the area overheads.
{"title":"ProMiSE: A Programmable Hardware Monitor for Secure Execution in Zero Trust Networks","authors":"Nikhilesh Singh;Shagnik Pal;Rainer Leupers;Farhad Merchant;Chester Rebeiro","doi":"10.1109/LES.2024.3354831","DOIUrl":"10.1109/LES.2024.3354831","url":null,"abstract":"With the inevitable adoption of zero trust architectures (ZTAs) for enterprise networks, there is a need to continuously gauge the security health of connected devices. This requires runtime monitoring of the devices in the network. The challenge, especially in resource-constrained environments, is to ensure trusted monitoring at a fine granularity. In this letter, we propose ProMiSE, a framework that overcomes this challenge and provides an online non-tamperable metric called trust score to quantify the security health of devices in a ZTA network. We use real-time hardware tracking of microarchitectural signals in the CPU to compute the trust score in a security co-processor that is isolated from the device’s computing stack. The trust score for each device is sent to the ZTA host for corresponding responses. We evaluate ProMiSE on an open-source RISC-V processor with different threat vectors, including ransomware, return-oriented programming (RoP) attacks, and cache-based microarchitectural attacks. We also deploy the framework on an AMD Artix 7AC701 FPGA and present the area overheads.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"433-436"},"PeriodicalIF":1.7,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.1109/LES.2024.3354179
Lucas A. Rabioglio;M. C. Cebedio;L. Arnone;L. De Micco;J. Castiñeira Moreira
This letter focuses on the field-programmable gate array (FPGA) implementation of a Class A Middleton noise estimator, aiming to enhance its efficiency and performance. The inherent algorithm of the estimator undergoes strategic enhancements, leveraging median approximations. This endeavor leads to the development of a more streamlined and expeditious architecture. The research not only introduces the refined architecture but also conducts a comparative analysis of its attributes. The outcomes of this investigation show the benefits of algorithmic optimization, as the execution times achieved in hardware significantly surpass those attainable through software-based implementation. This underscores the practicality of the algorithmic refinement and also the notable advantages of the FPGA-based execution in terms of computational speed.
这封信的重点是现场可编程门阵列(FPGA)的 A 类 Middleton 噪声估计器的实现,旨在提高其效率和性能。利用中值近似对估计器的固有算法进行了战略性改进。这一努力导致开发出一种更精简、更快速的架构。研究不仅介绍了改进后的架构,还对其属性进行了比较分析。研究结果表明了算法优化的好处,因为在硬件上实现的执行时间大大超过了通过软件实现的执行时间。这凸显了算法改进的实用性,以及基于 FPGA 的执行在计算速度方面的显著优势。
{"title":"Middleton Class A Noise Median Estimator: FPGA and Software Implementation","authors":"Lucas A. Rabioglio;M. C. Cebedio;L. Arnone;L. De Micco;J. Castiñeira Moreira","doi":"10.1109/LES.2024.3354179","DOIUrl":"10.1109/LES.2024.3354179","url":null,"abstract":"This letter focuses on the field-programmable gate array (FPGA) implementation of a Class A Middleton noise estimator, aiming to enhance its efficiency and performance. The inherent algorithm of the estimator undergoes strategic enhancements, leveraging median approximations. This endeavor leads to the development of a more streamlined and expeditious architecture. The research not only introduces the refined architecture but also conducts a comparative analysis of its attributes. The outcomes of this investigation show the benefits of algorithmic optimization, as the execution times achieved in hardware significantly surpass those attainable through software-based implementation. This underscores the practicality of the algorithmic refinement and also the notable advantages of the FPGA-based execution in terms of computational speed.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 3","pages":"275-278"},"PeriodicalIF":1.7,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.1109/LES.2024.3354081
Feroz Ahmad;Saima Zafar
Real-time monitoring of 1-D biopotentials, such as electrocardiograms (ECG), necessitates effective feature extraction and classification, a strength of deep learning (DL) algorithms. Designing 1-D convolutional neural network (1-D CNN) accelerators for biopotential classification via open-source codesign workflows, particularly high-level synthesis for machine learning (HLS4ML), offers advantages over GPU-based or cloud-based solutions, including high performance, low latency, low power consumption, swift time-to-market, and cost-effectiveness. We present an implementation of a quantized-pruned (QP) 1-D CNN model on the PYNQ Z2 SoC using HLS4ML by seamlessly deploying its soft IP core generated via Vivado Accelerator backend, showcasing the efficacy of quantization-aware training (QAT) in reducing power consumption to 1.655 W from 1.823 W. Our approach demonstrates improved area consumption, resource utilization, and inferences per second compared to the baseline (B) 1-D CNN model, with a controlled 4% or less reduction in weighted Accuracy, Precision, Recall, and F1-score, revealing the nuanced tradeoffs between performance metrics and system efficiency for real-time 3-channel ECG Arrhythmia classification.
{"title":"SoC-Based Implementation of 1-D Convolutional Neural Network for 3-Channel ECG Arrhythmia Classification via HLS4ML","authors":"Feroz Ahmad;Saima Zafar","doi":"10.1109/LES.2024.3354081","DOIUrl":"10.1109/LES.2024.3354081","url":null,"abstract":"Real-time monitoring of 1-D biopotentials, such as electrocardiograms (ECG), necessitates effective feature extraction and classification, a strength of deep learning (DL) algorithms. Designing 1-D convolutional neural network (1-D CNN) accelerators for biopotential classification via open-source codesign workflows, particularly high-level synthesis for machine learning (HLS4ML), offers advantages over GPU-based or cloud-based solutions, including high performance, low latency, low power consumption, swift time-to-market, and cost-effectiveness. We present an implementation of a quantized-pruned (QP) 1-D CNN model on the PYNQ Z2 SoC using HLS4ML by seamlessly deploying its soft IP core generated via Vivado Accelerator backend, showcasing the efficacy of quantization-aware training (QAT) in reducing power consumption to 1.655 W from 1.823 W. Our approach demonstrates improved area consumption, resource utilization, and inferences per second compared to the baseline (B) 1-D CNN model, with a controlled 4% or less reduction in weighted Accuracy, Precision, Recall, and F1-score, revealing the nuanced tradeoffs between performance metrics and system efficiency for real-time 3-channel ECG Arrhythmia classification.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"429-432"},"PeriodicalIF":1.7,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.1109/LES.2024.3352540
Mohd. Tasleem Khan;Jinti Hazarika
In this work, we present an area and energy-efficient serial multiplier. Specifically, we exploit symmetries in odd and even partial products (PPs) in its radix- $gamma $