Scholarly Works, Electrical and Computer Engineering

Permanent URI for this collection

https://hdl.handle.net/10919/23145

Research articles, presentations, and other scholarship

Browse

Now showing 1 - 20 of 923

Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning
Zhang, Chaoyu; Shi, Shanghao; Wang, Ning; Xu, Xiangxiang; Li, Shaoyu; Zheng, Lizhong; Marchany, Randy; Gardner, Mark; Hou, Y. Thomas; Lou, Wenjing (ACM, 2024-10-14)
Anomaly-Based Intrusion Detection Systems (IDSs) have been extensively researched for their ability to detect zero-day attacks. These systems establish a baseline of normal behavior using benign traffic data and flag deviations from this norm as potential threats. They generally experience higher false alarm rates than signature-based IDSs. Unlike image data, where the observed features provide immediate utility, raw network traffic necessitates additional processing for effective detection. It is challenging to learn useful patterns directly from raw traffic data or simple traffic statistics (e.g., connection duration, package inter-arrival time) as the complex relationships are difficult to distinguish. Therefore, some feature engineering becomes imperative to extract and transform raw data into new feature representations that can directly improve the detection capability and reduce the false positive rate. We propose a geometric feature learning method to optimize the feature extraction process. We employ contrastive feature learning to learn a feature space where normal traffic instances reside in a compact cluster. We further utilize H-Score feature learning to maximize the compactness of the cluster representing the normal behavior, enhancing the subsequent anomaly detection performance. Our evaluations using the NSL-KDD and N-BaloT datasets demonstrate that the proposed IDS powered by feature learning can consistently outperform state-of-the-art anomaly-based IDS methods by significantly lowering the false positive rate. Furthermore, we deploy the proposed IDS on a Raspberry Pi 4 and demonstrate its applicability on resource-constrained Internet of Things (IoT) devices, highlighting its versatility for diverse application scenarios.
libLISA: Instruction Discovery and Analysis on x86-64
Craaijo, Jos; Verbeek, Freek; Ravindran, Binoy (ACM, 2024-10-08)
Even though heavily researched, a full formal model of the x86-64 instruction set is still not available. We present libLISA, a tool for automated discovery and analysis of the ISA of a CPU. This produces the most extensive formal x86-64 model to date, with over 118000 different instruction groups. The process requires as little human specification as possible: specifically, we do not rely on a human-written (dis)assembler to dictate which instructions are executable on a given CPU, or what their in- and outputs are. The generated model is CPU-specific: behavior that is "undefined" is synthesized for the current machine. Producing models for five different x86-64 machines, we mutually compare them, discover undocumented instructions, and generate instruction sequences that are CPU-specific. Experimental evaluation shows that we enumerate virtually all instructions within scope, that the instructions' semantics are correct w.r.t. existing work, and that we improve existing work by exposing bugs in their handwritten models.
Octopus-Inspired Adhesives with Switchable Attachment to Challenging Underwater Surfaces
Lee, Chanhong; Via, Austin C.; Heredia, Aldo; Adjei, Daniel A.; Bartlett, Michael D. (Wiley-VCH, 2024-10-09)
Adhesives that excel in wet or underwater environments are critical for applications ranging from healthcare and underwater robotics to infrastructure repair. However, achieving strong attachment and controlled release on difficult substrates, such as those that are curved, rough, or located in diverse fluid environments, remains a major challenge. Here, an octopus-inspired adhesive with strong attachment and rapid release in challenging underwater environments is presented. Inspired by the octopus’s infundibulum structure, a compliant, curved stalk, and an active deformable membrane for multi-surface adhesion are utilized. The stalk’s curved shape enhances conformal contact on large-scale curvatures and increases contact stress for adaptability to small-scale roughness. These synergistic mechanisms improve contact across multiple length scales, resulting in switching ratios of over 1000 within ≈30 ms with consistent attachment strength of over 60 kPa on diverse surfaces and conditions. These adhesives are demonstrated through the robust attachment and precise manipulation of rough underwater objects.
Offloading Datacenter Jobs to RISC-V Hardware for Improved Performance and Power Efficiency
Heerekar, Balvansh; Philippidis, Cesar; Chuang, Ho-Ren; Olivier, Pierre; Barbalace, Antonio; Ravindran, Binoy (ACM, 2024-09-16)
The end of Moore’s Law has brought significant changes in the architecture of servers used in data centers, increasingly incorporating new ISAs beyond x86-64 as well as diverse accelerators. Further, single-board computers have become increasingly efficient and can run certain Linux applications at significantly lower equipment and energy costs compared to traditional servers. Past research has demonstrated that offloading applications at runtime from x86-based servers to ARM-based single-board computers can result in increases in throughput and energy efficiency. The RISC-V architecture has recently gained significant commercial interest, and OS-capable single-board computers with RISC-V cores are increasingly available at the commodity scale. In this paper we propose a system that offloads jobs from an x86 server to a RISC-V single-board computer at runtime, with the goals of improving job throughput and energy saved. Towards this, we port the Popcorn Linux multi-ISA toolchain and runtime framework to RISC-V, enabling the live migration of applications between an x86 Xeon server and a SiFive HiFive RISC-V board. We further propose a scheduling policy, Lowest Slowdown First (LSF) that drives the offloading of long-running and stateful datacenter background jobs from the server to the board, to alleviate workload congestion on the server. LSF’s policy relies on monitoring jobs’ performance on the server, predicting the slowdown they would suffer if running on the board, and migrating the jobs with the lowest estimated slowdown. Our evaluation shows that LSF yields up to 20% increase in throughput while also gaining 16% more energy efficiency for compute-intensive workloads.
Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation
Wong, Lauren J.; Muller, Braeden P.; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-07-25)
The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques and applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work examines whether existing transferability metrics, used in other modalities, might be useful in the context of RFML. Results show that the two existing metrics tested, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), correlate well with post-transfer accuracy and can therefore be used to select source models for radio frequency (RF) domain adaptation and to predict post-transfer accuracy.
Asymmetric Optical Scanning Holography Encryption with Elgamal Algorithm
Wu, Chunying; Ding, Yinggang; Yan, Aimin; Poon, Ting-Chung; Tsang, Peter Wai Ming (MDPI, 2024-09-19)
This paper proposes an asymmetric scanning holography cryptosystem based on the Elgamal algorithm. The method encodes images with sine and cosine holograms. Subsequently, each hologram is divided into a signed bit matrix and an unsigned hologram matrix, both encrypted using the sender’s private key and the receiver’s public key. The resulting ciphertext matrices are then transmitted to the receiver. Upon receipt, the receiver decrypts the ciphertext matrices using their private key and the sender’s public key. We employ an asymmetric single-image encryption method for key management and dispatch for securing imaging and transmission. Furthermore, we conducted a sensitivity analysis of the encryption system. The image encryption metrics, including histograms of holograms, adjacent pixel correlation, image correlation, the peak signal-to-noise ratio, and the structural similarity index, were also examined. The results demonstrate the security and stability of the proposed method.
Epigenomic tomography for probing spatially defined chromatin state in the brain
Liu, Zhengzhi; Deng, Chengyu; Zhou, Zirui; Ya, Xiao; Jiang, Shan; Zhu, Bohan; Naler, Lynette B.; Jia, Xiaoting; Yao, Danfeng (Daphne); Lu, Chang (Cell Press, 2024-03-25)
Spatially resolved epigenomic profiling is critical for understanding biology in the mammalian brain. Singlecell spatial epigenomic assays were developed recently for this purpose, but they remain costly and labor intensive for examining brain tissues across substantial dimensions and surveying a collection of brain samples. Here, we demonstrate an approach, epigenomic tomography, that maps spatial epigenomes of mouse brain at the scale of centimeters. We individually profiled neuronal and glial fractions of mouse neocortex slices with 0.5 mm thickness. Tri-methylation of histone 3 at lysine 27 (H3K27me3) or acetylation of histone 3 at lysine 27 (H3K27ac) features across these slices were grouped into clusters based on their spatial variation patterns to form epigenomic brain maps. As a proof of principle, our approach reveals striking dynamics in the frontal cortex due to kainic-acid-induced seizure, linked with transmembrane ion transporters, exocytosis of synaptic vesicles, and secretion of neurotransmitters. Epigenomic tomography provides a powerful and cost-effective tool for characterizing brain disorders based on the spatial epigenome.
Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment
Amini, Hossein; Mehrizi-Sani, Ali; Noroozian, Reza (MDPI, 2024-09-03)
Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.
Multi-Hop User Equipment (UE) to UE Relays for MANET/Mesh Leveraging 5G NR Sidelink
Shyy, DJ; Luu, Cuong; Xu, John D.; Liu, Lingjia; Erpek, Tugba; Gabay, David; Bate, David (ACM, 2023-12-06)
This paper provides use cases to adapt 5G sidelink technology to enable multi-hop User Equipment (UE)-to-UE (U2U) and UE-to- Network relaying in 3GPP standards. Such a capability could enable groups of users to communicate with each other when operating at the periphery or outside a network’s coverage area, with commercial and public safety benefits. This paper compares routing protocols to enable sidelink with U2U relay to support a Mobile Ad hoc Network (MANET). A gap analysis of current 3rd Generation Partnership Project (3GPP) Release 18 (R-18) specifications is performed to determine the missing procedures to enable multi-hop U2U relaying, along with a proposed candidate protocol to fill the gap. The candidate protocol can be submitted as a contribution to 3GPP TSG Service and System Aspects (SA) Working Group 2 (WG2) as proposed changes to the 5G architecture in 3GPP Release 19 (R-19).
Spiking Neural Encoding Schemes and STDP Training Algorithms for Edge Computing
Zheng, Honghao; Yi, Yang (ACM, 2023-12-06)
To enhance real-time data processing, edge computing is utilized in a wider and wider range of applications. For the areas that require large bandwidth and low latency, edge computing even becomes a must. For instance, in the communication area, spectrum sharing within multiple users requires high accuracy of spectrum using prediction as well as low latency. For such tasks, neuromorphic computing, especially spiking neural networks (SNNs), can be a potential method because of its power and silicon area efficiency. In this paper, we have discussed various kinds of spiking neural encoding schemes and their integrated circuit (IC) implementations. We have also summarized the pair-based STDP and the triplet-based STDP learning rule, their mathematical models, and the triplet-based reconfigurable circuit implementation. The Pytorch simulation of different encoding schemes working with two STDP rules for the MNIST and a dynamic spectrum sensing dataset is also presented. It shows that multiplexing ISI-phase encoder can achieve at most 8.9% higher accuracy than other encoders, and TSTDP provides 2.7% higher accuracy than PSTDP for the MNIST dataset. What’s more, for the task of spectrum sensing for edge computing, the multiplexing encoding is also 4.3% more accurate, and TSTDP is 0.3% more accurate for the spectrum utilization prediction.
T-DOpE probes reveal sensitivity of hippocampal oscillations to cannabinoids in behaving mice
Kim, Jongwoon; Huang, Hengji; Gilbert, Earl T.; Kaiser C., Arndt; English, Daniel Fine; Jia, Xiaoting (Nature Research, 2024-02-24)
Understanding the neural basis of behavior requires monitoring and manipulating combinations of physiological elements and their interactions in behaving animals. We developed a thermal tapering process enabling fabrication of low-cost, flexible probes combining ultrafine features: dense electrodes, optical waveguides, and microfluidic channels. Furthermore, we developed a semi-automated backend connection allowing scalable assembly. We demonstrate T-DOpE (Tapered Drug delivery, Optical stimulation, and Electrophysiology) probes achieve in single neuron-scale devices (1) highfidelity electrophysiological recording (2) focal drug delivery and (3) optical stimulation. The device tip can beminiaturized (as small as 50 μm) tominimize tissue damage while the ~20 times larger backend allows for industrial-scale connectorization. T-DOpE probes implanted in mouse hippocampus revealed canonical neuronal activity at the level of local field potentials (LFP) and neural spiking. Taking advantage of the triple-functionality of these probes, we monitored LFP while manipulating cannabinoid receptors (CB1R; microfluidic agonist delivery) and CA1 neuronal activity (optogenetics). Focal infusion of CB1R agonist downregulated theta and sharp wave-ripple oscillations (SPWRs). Furthermore, we found that CB1R activation reduces sharp wave-ripples by impairing the innate SPW-R-generating ability of the CA1 circuit.
Implementation and Testing of GBDI Memory Compression Algorithm
Panja, Promit; Chiu, TingHung (2023-05-15)
This project aims to implement and test a lossless memory compression technique called GBDI (Global Base-Delta-Immediate) using C. GBDI compresses data by only storing the difference (deltas) between the global base value and the actual values in the memory block and is an extension of the BDI memory compression algorithm. GBDI enables high compression ratios and low decompression latencies, which can improve memory bandwidth and performance. The project involves implementing the GBDI compressor and decompressor, evaluating their performance on C++ and Java benchmarks and comparing them to the results the authors have shown.
Modeling and Formal Verification of Vehicle Platooning System
Panja, Promit; Bhavandlapelli, Rakesh Kumar (2023-05-15)
Vehicle platooning, which involves operating a group of vehicles at close distances, reduces aerodynamic drag and hence decreases fuel consumption and greenhouse gas emissions. However, implementing such a system in the real world requires careful attention to safety. In this project, we aim to analyze, simulate, and formally verify a vehicle platooning system for the follower vehicle that can maintain a safe distance from the lead vehicle while taking into account control decisions and communication delays between the two vehicles.
Realtime Detection of PMU Bad Data and Sequential Bad Data Classifications in Cyber-Physical Testbed
Khan, Imtiaj; Centeno, Virgilio (IEEE, 2023-07-18)
Modern Smart Grids incorporate physical power grids and cyber systems, creating a cyberphysical system. Phasor measurement units (PMUs) transmit time synchronized measurement data from physical grid to the cyber system. The System Operator (SO) in the cyber layer analyzes the data in both online and offline format and ensures the reliability and security of the grid by sending necessary command back to the PMUs. However, various physical events such as line to ground faults, frequency events, transformer events as well as cyberattacks can cause deviation in measurements received by the SO, which can be termed as ‘‘bad data’’. These bad data in turn can cause the SO to take a wrong restorative/ mitigating strategy. Therefore accurate detection of bad data and identification of correct bad data type is necessary to ensure grid’s safety and optimal performance. In this work we proposed a realtime sequential bad data detection and bad data classification strategy. At first, we have exploited the low rank property of Hankel-matrix to detect the occurrence of bad data in realtime. Secondly, we classify the bad data into two categories: physical events and cyberattacks. The algorithm utilizes the difference in low rank approximation error of multi-channel Hankel-matrix before and after random column permutations during physical events. If the cause of bad data is identified as cyberattack, our proposed algorithm proceeds to identify the cause of cyberattack. We have considered two possible cyberattack types: false data injection attack (FDIA) and GPS-spoofing attack (GSA). The proposed algorithm observes rank-1 approximation error of single-channel Hankel matrix containing unwrapped phase angle data to distinguish FDIA from GSA. Finally, the proposed algorithm is implemented in a realtime cyber-physical testbed containing PMU simulator and openECA. Results from the testbed using IEEE 13 node test feeder show that by choosing optimum parameters of Hankel-matrix, the bad data can be detected as well as the type of bad data can be correctly identified within less than 1 sec. of the occurrence of physical event or cyberattack. The bad data detection shows 100% accuracy for Hankel-matrix data-window greater than 140. Bad data can be classified as either cyberattack or physical event with perfect accuracy for data-window length greater than 73 for the threshold 0.1. A data-window length between 80 to 120 can distinguish GSA from FDIA, while GSA is implemented with varying phase angle shift of 0.1⁰ to 0.5⁰. The realtime sequential model is also verified with IEEE 118 bus system simulated with SIEMENS PSS/E. Due to more complicated grid structure, IEEE 118 system requires more computational time to identify the bad data type, however that is still less than 2 sec, and can perform detection and classification with data-window length as small as 40.
Analysis of a Grid-Connected Solar PV System with Battery Energy Storage for Irregular Load Profile
Alhazmi, Mohannad; Alfadda, Abdullah; Alfakhri, Abdullah (MDPI, 2024-07-14)
In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region’s abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season.
Measuring Broadband America: A Retrospective on Origins, Achievements, and Challenges
Burger, Eric W.; Krishnaswamy, Padma; Schulzrinne, Henning (ACM, 2023-04)
The "Measuring Broadband America" program, run by the United States Federal Communications Commission (FCC), continually measures and releases data on the performance of consumer broadband access networks in the US. This paper presents a retrospective on the program, from its beginnings in 2010 to the present. It also reviews the underlying measurement approaches, philosophies, distinguishing features, and lessons learned over the program's duration thus far. We focus on fixed broadband access since it is the program component with the longest history. We also discuss future directions and challenges.
Covert and Quantum-Safe Tunneling of Multi-Band Military-RF Communication Waveforms Through Non-Cooperative 5G Networks
Alwan, Elias; Volakis, John; Islam, Md Khadimul; De Silva, Udara; Madanayake, Arjuna; Sanchez, Jose Angel; Sklivanitis, George; Pados, Dimitris A.; Beckwith, Luke; Azarderakhsh, Reza; Muralkrishan, Madhuvanti; Rastogi, Rishabh; Hore, Aniruddha; Burger, Eric W. (IEEE, 2023)
We have built a prototype universal radio adapter which furnishes seamless and secure wireless communication through non-cooperative indigenous 5G networks for military and government users. The adapter consists of a waveform-agnostic hardware add-on that tunnels DoD terrestrial and satellite data. The adapter uses secure protocols for cross-connecting military-grade wireless RF communications equipment using spectrum in the range from UHF to Ka-band. A 5G data transport channel replaces the captured spectrum for transporting information at the IQ-sample level. In a sense, we replace the antenna-air interface and wireless channel with a transparent 5G data network. A plurarity of legacy military systems can operate through modern 5G networks in a seamless way without any knowledge of the characteristics of military waveforms. The adapter incorporates AI/ML based methods for smart spectrum sensing and autonomous radio reconfiguration. This enables intelligent interconnection of a number of military radios through non-cooperative (potentially adversarial) 5G commercial cellular networks. The adapter is built on four technical pillars: 1) ultra-wideband apertures for multi-functional and flexible software-defined radios (SDRs) with agile, wideband, and dual-band tunable RF transceivers for FR1/FR2 bands; 2) physical layer operation that involve device authentication via deep-learning based RF fingerprinting and compression of acquired IQ data; 3) secure and reconfigurable cryptographic co-processors employing the new quantum-safe algorithms selected by NIST to achieve authentication, key exchange, and encryption with focus on resource-constrained low size, weight, power, and cost (SWaP-C) devices; and 4) generative artificial intelligence and spread-spectrum steganography to hide DoD traffic passed through 5G networks and improve resiliency against real-time traffic analysis by nation-state carriers and intelligence agencies.
Spectrum Sharing of the 12 GHz Band with Two-Way Terrestrial 5G Mobile Services: Motivations, Challenges, and Research Road Map
Hassan, Zoheb; Heeren-Moon, Erika; Sabzehali, Javad; Shah, Vijay K.; Dietrich, Carl; Reed, Jeffrey H.; Burger, Eric W. (IEEE, 2023-07)
Telecommunication industries and spectrum regulation authorities are increasingly interested in unlocking the 12 GHz band for two-way 5G terrestrial services. The 12 GHz band has a much larger bandwidth than the current sub-6 GHz band and better propagation characteristics than the millimeter-wave (mmWave) band. Thus, the 12 GHz band offers great potential for improving the coverage and capacity of terrestrial 5G networks. However, interference issues between incumbent receivers and 5G radio links present a major challenge in the 12 GHz band. If one could exploit the dynamic contexts inherent to the 12 GHz band, one could reform spectrum sharing policy to create spectrum access opportunities for 5G mobile services. This article makes three contributions. First, it presents the characteristics and challenges of the 12 GHz band. Second, we explain the characteristics and requirements for spectrum sharing at a variety of levels to resolve those issues. Lastly, we present several research opportunities to enable harmonious coexistence of incumbent licensees and 5G networks within the 12 GHz band.
TriSAS: Toward Dependable Inter-SAS Coordination with Auditability
Shi, Shanghao; Xiao, Yang; Du, Changlai; Shi, Yi; Wang, Chonggang; Gazda, Robert; Hou, Y. Thomas; Burger, Eric W.; Dasilva, Luiz; Lou, Wenjing (ACM, 2024-07-01)
To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem — the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize faulttolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms. In this work, we propose TriSAS—a novel inter-SAS coordination mechanism to facilitate secure, efficient, and dependable spectrum allocation that is fully compatible with the existing SAS infrastructure. TriSAS decomposes the coordination process into two phases including input synchronization and decision finalization. The first phase ensures participants share a common input set while the second one fulfills a fair and verifiable spectrum allocation selection, which is generated efficiently via SAS proposers’ proprietary allocation algorithms and evaluated by a customized designed allocation evaluation algorithm (AEA), in the face of no more than one-third of malicious participants. We implemented a prototype of TriSAS on the AWS cloud computing platform and evaluated its throughput and latency performance. The results show that TriSAS achieves high transaction throughput and low latency under various practical settings.
Secure Data-Binding in FPGA-based Hardware Architectures utilizing PUFs
Frank, Florian; Schmid, Martin; Klement, Felix; Palani, Purushothaman; Weber, Andreas; Kavun, Elif Bilge; Xiong, Wenjie; Arul, Tolga; Katzenbeisser, Stefan (ACM, 2024-07-01)
In this work, a novel FPGA-based data-binding architecture incorporating PUFs and a user-specific encryption key to protect the confidentiality of data on external non-volatile memories is presented. By utilizing an intrinsic PUF derived from the same memory, the confidential data is additionally bound to the device. This feature proves valuable in cases where software is restricted to be executed exclusively on specific hardware or privacy-critical data is not allowed to be decrypted elsewhere. To improve the resistance against hardware attacks, a novel method to randomly select memory cells utilized for PUF measurements is presented. The FPGA-based design presented in this work allows for low latency as well as small area utilization, offers high adaptability to diverse hardware and software platforms, and is accessible from bare-metal programs to full Linux kernels. Moreover, a detailed performance and security evaluation is conducted on five boards. A single read or write operation can be executed in 0.58 𝜇𝑠 when utilizing the lightweight PRINCE cipher on an AMD Zync 7000 MPSoC. Furthermore, the entire architecture occupies only about 10% of the FPGA’s available space on a resource-constrained AMD PYNQ-Z2. Ultimately, the implementation is demonstrated by storing confidential user data on new generations of network base stations equipped with FPGAs

Browse

Recent Submissions