Scholarly Works, Electrical and Computer Engineering

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

Research articles, presentations, and other scholarship

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Eavesdropper Avoidance through Adaptive Beam Management in SDR-Based MmWave Communications
Baron-Hyppolite, Adrian; Santos, Joao F.; DaSilva, Luiz A.; Kibiłda, Jacek (IEEE, 2024-01-01)
High-frequency systems use beamforming to mitigate the increased path loss. As the resulting beams become highly directional, Millimeter Wave (mmWave) radios conduct a beam sweep to probe all possible angular directions to locate each other and establish communication. In this paper, we propose an adaptive beam management strategy that leverages beam sweeping to avoid eavesdroppers and other potential attackers. Our solution employs Deep Reinforcement Learning (DRL) to dynamically select a subset of beams in the transmitter codebook. We evaluate this solution through a proof-of-concept implementation using a combination of Software-Defined Radios (SDRs) and commercial mmWave equipment, and show the improvements in the secrecy capacity.
Experimental Validation of a 3GPP compliant 5G-based Positioning System
Dhungel, Sarik; Duggal, Gaurav; Ron, Dara; Tripathi, Nishith; Buehrer, R. Michael; Reed, Jeffrey H.; Shah, Vijay K. (ACM, 2024-12-04)
The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.
Automated and Blind Detection of Low Probability of Intercept RF Anomaly Signals
Gusain, Kuanl; Hassan, Zoheb; Couto, David; Malek, Mai Abdel; Shah, Vijay K; Zheng, Lizhong; Reed, Jeffrey H. (ACM, 2024-12-04)
Automated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB).
sMVX: Multi-Variant Execution on Selected Code Paths
Yeoh, Sengming; Wang, Xiaoguang; Jang, Jae-Won; Ravindran, Binoy (ACM, 2024-12-02)
Multi-Variant Execution (MVX) is an effective way to detect memory corruption vulnerabilities, intrusions, or live software updates. A traditional MVX system concurrently runs multiple copies of functionally identical, layout-different program variants. Therefore, a typical memory corruption attack that forges pointers can succeed on at most one variant, leading the other variant(s) to crash. The replicated execution adds software security and reliability but also brings multiple times of CPU and memory usage. This paper presents sMVX, a flexible multi-variant execution system replicating variants only on the selected code paths. sMVX allows end-users to annotate a target program and indicate sensitive code regions for multi-variant execution. Such code regions can be authentication-related code or sensitive functions that handle potentially malicious input data. An sMVX runtime only replicates the sensitive functions and executes them in lockstep. We have implemented a prototype of sMVX using an in-process code monitor. The sMVX monitor supports the selected code paths MVX from within the target program’s address space, but the monitor is isolated from the target’s code by the Intel Memory Protection Keys (MPK). We evaluated the sMVX using a benchmark suite and two server applications. The evaluation demonstrates that sMVX exhibits a comparable performance overhead to state-of-the-art MVX systems but requires 20% fewer CPU cycles and 49% less memory consumption on server applications.
Verifiably Correct Lifting of Position-Independent x86-64 Binaries to Symbolized Assembly
Verbeek, Freek; Naus, Nico; Ravindran, Binoy (ACM, 2024-12-02)
We present an approach to lift position-independent x86-64 binaries to symbolized NASM. Symbolization is a decompilation step that enables binary patching: functions can be modified, and instructions can be interspersed. Moreover, it is the first abstraction step in a larger decompilation chain. The produced NASM is recompilable, and we extensively test the recompiled binaries to see if they exhibit the same behavior as the original ones. In addition to testing, the produced NASM is accompanied with a certificate, constructed in such a way that if all theorems in the certificate hold, symbolization has occurred correctly. The original and recompiled binary are lifted again with a third-party decompiler (Ghidra). These representations, as well as the certificate, are loaded into the Isabelle/HOL theorem prover, where proof scripts ensure that correctness can be proven automatically. We have applied symbolization to various stripped binaries from various sources, from various compilers, and ranging over various optimization levels.We show how symbolization enables binary-level patching, by tackling challenges originating from industry.
Machine Learning-Driven Optimization of Livestock Management: Classification of Cattle Behaviors for Enhanced Monitoring Efficiency
Zhao, Zhuqing; Shehada, Halah; Ha, Dong; Dos Reis, Barbara; White, Robin; Shin, Sook (ACM, 2024-08-02)
Monitoring cattle health in remote and expansive pastures poses significant challenges that necessitate automated, continuous, and real-time behavior monitoring. This paper investigates the effectiveness and reliability sensor-based cattle behavior classification for such monitoring, emphasizing the impact of intelligent feature selection in enhancing classification performance. To achieve this, we developed Wireless Sensor Nodes (WSN) affixed to individual cattle, enabling the capture of 3-axis acceleration data from five cows across varying seasons, spanning from summer to winter. Initially, we extracted a comprehensive set of 52 features, representing a broad spectrum of cow behaviors alongside statistical attributes. To enhance computational efficiency, we employed the Recursive Feature Elimination (RFE) method to distill 30 critical features by discarding redundant or less significant ones. Subsequently, these optimized features were utilized to train four machine learning (ML) models: Support Vector Machine (SVM), k-Nearest Neighbors (k- NN), Random Forest (RF), and Histogram-based Gradient Boosted Decision Trees (HGBDT). Notably, the HGBDT model demonstrated superior performance, achieving remarkable F1-scores of 99.01% for ’grazing’, 98.74% for ’ruminating’, 89.62% for ’lying’, 84.06% for ’standing’, and 91.87% for ’walking’. These findings underscore the potential of our approach to serve as a robust framework for precision livestock farming, offering valuable insights into enhancing cattle health monitoring in remote environments.
SegIt: Empowering Sensor Data Labeling with Enhanced Efficiency and Security
Zhang, Zhen; Abraham, Samuel; Lee, Alex; Li, Yichen; Morota, Gota; Ha, Dong; Shin, Sook (ACM, 2024-08-02)
SegIt is a novel, user-friendly, and highly efficient sensor data labeling tool designed to tackle critical challenges such as data privacy, synchronization accuracy, and memory efficiency inherent in existing labeling tools. While many current sensor data labeling tools provide free online services, they typically necessitate users to upload unlabeled sensor data, alongside video or audio references, to cloud storage for labeling. Nevertheless, such third-party storage exposes user data to potential security risks. SegIt, an innovative open-source tool, provides a software solution for tagging unlabeled sensor data directly on a local computer, ensuring enhanced accuracy, convenience, and, most importantly, data security.
Energy Backflow in Unidirectional Monochromatic and Space–Time Waves
Saari, Peeter; Besieris, Ioannis M. (MDPI, 2024-11-29)
Backflow, or retropropagation, is a counterintuitive phenomenon whereby for a forward-propagating wave the energy locally propagates backward. In the context of backflow, physically most interesting are the so-called unidirectional waves, which contain only forward-propagating plane wave constituents. Yet, very few such waves possessing closed-form analytic expressions for evaluation of the Poynting vector are known. In this study, we examine energy backflow in a novel (2+time)-dimensional unidirectional monochromatic wave and in a (2+1)D spatiotemporal wavepacket, analytic expressions which we succeeded to find. We also present a detailed study of the backflow in the “needle” pulse. This is an interesting model object because well-known superluminal non-diffracting space–time wave packets can be derived from its simple factored wave function. Finally, we study the backflow in an unidirectional version of the so-called focus wave mode—a pulse propagating luminally and without spread, which is the first and most studied representative of the (3+1)D non-diffracting space–time wave packets (also referred to as spatiotemporally localized waves).
Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms
Donekal Chandrashekar, Nikitha; Lee, Anthony; Azab, Mohamed; Gracanin, Denis (MDPI, 2024-12-18)
In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure.
Modeling and Analysis of Dispersive Propagation of Structural Waves for Vibro-Localization
Ambarkutuk, Murat; Plassmann, Paul E. (MDPI, 2024-12-04)
The dispersion of structural waves, where wave speed varies with frequency, introduces significant challenges in accurately localizing occupants in a building based on vibrations caused by their movements. This study presents a novel multi-sensor vibro-localization technique that accounts for dispersion effects, enhancing the accuracy and robustness of occupant localization. The proposed method utilizes a model-based approach to parameterize key propagation phenomena, including wave dispersion and attenuation, which are fitted to observed waveforms. The localization is achieved by maximizing the joint likelihood of the occupant’s location based on sensor measurements. The effectiveness of the proposed technique is validated using two experimental datasets: one from a controlled environment involving an aluminum plate and the other from a building-scale experiment conducted at Goodwin Hall, Virginia Tech. Results for the proposed algorithm demonstrates a significant improvement in localization accuracy compared to benchmark algorithms. Specifically, in the aluminum plate experiments, the proposed technique reduced the average localization precision from 7.77 cm to 1.97 cm, representing a ∼74% improvement. Similarly, in the Goodwin Hall experiments, the average localization error decreased from 0.67 m to 0.3 m, with a ∼55% enhancement in accuracy. These findings indicate that the proposed approach outperforms existing methods in accurately determining occupant locations, even in the presence of dispersive wave propagation.
Global Sensitivity Analysis for Integrated Heat and Electricity Energy System
Li, Yibo; Xu, Yijun; Yao, Shuai; Lu, Shuai; Gu, Wei; Mili, Lamine M.; Korkali, Mert (IEEE, 2024-11-18)
Although global sensitivity analysis (GSA) is gaining increasing popularity in power systems due to its ability to measure the importance of uncertain inputs, it has not been explored in the integrated energy system (IES) in the existing literature. Indeed, when coupled multi-energy systems (e.g., heating networks) are considered, the power system operation states are inevitably altered. Accordingly, its associated GSA, which relies on Monte Carlo simulations (MCS), becomes even more computationally prohibitive since it not only increases the model complexity but also faces large uncertainties. To address these issues, this paper proposes a double-loop generalized unscented transform (GenUT)-based strategy that, for the first time, explores the GSA in the IES while simultaneously achieving high computing efficiency and accuracy. More specifically, we first propose a GenUT method that can propagate the moment information of correlated input variables following different types of probability distributions in the IES. We further design a double-loop sampling scheme for GenUT to evaluate the GSA for correlated uncertainties in a cost-effective manner. The simulations of multiple heat- and power-coupled IESs reveal the excellent performance of the proposed method
Robust Constant Curvature Curve Communications with Complex and Quaternion Neural Networks
Buvarp, Anders M.; Mili, Lamine M.; Zaghloul, Amir I. (IEEE, 2024-06-25)
The concept of Digital Twin has recently emerged, which requires the transmission of a massive amount of sensor data with low latency and high reliability. Analog error correction is an attractive method for low-latency communications; hence, in this paper, we propose the use of complex-valued neural networks and Quaternionic Neural Networks (QNNs) to decode analog codes. Furthermore, we propose mapping our codes to the baseband of the frequency domain to enable easy time and frequency synchronization as well as to mitigate frequency-selective fading using robust estimation theory. This is accomplished by applying inverse Discrete Fourier Transform (DFT) modulation, which achieves a significant reduction in hardware complexity, power, and cost as compared to our previously proposed analog coding scheme. Additionally, we introduce a scaled version of our previous analog codes that enables statistical signal processing, something we have not been able to achieve until now. This achieves significant noise immunity with drastic performance improvements at low Signal-to-Noise Ratios (SNR) and a small loss at high SNR.
A Low-Rank Tensor Train Approach for Electric Vehicle Load Data Reconstruction Using Real Industrial Data
Sun, Bo; Xu, Yijun; Gu, Wei; Cai, Huihuang; Lu, Shuai; Mili, Lamine M.; Yu, Wenwu; Wu, Zhi (IEEE, 2024-09-30)
As electric vehicles (EVs) gain popularity, their interaction with the power system cannot be overlooked. Therefore, there is a growing need for accurate EV load data to facilitate precise operation and control in power systems. However, in practice, due to the high cost of high-frequency measurement devices and limited data storage capacity, only low-resolution metered EV data are available. To address this, this paper proposed a tensor completion-based method for EV load data reconstruction. More specifically, we first reformulate the load data as high-dimensional tensors and consider unknown data to be recovered as missing entries. Subsequently, we leverage the low-rank properties of high-dimensional data to perform tensor completion. To achieve this, two optimization formulations are proposed: a nuclear norm minimization algorithm based on singular value thresholding (SVT) and a tensor rank approximation algorithm via parallel matrix factorization. Both approaches are based on the tensor train (TT) rank, thanks to its well-balanced matricization scheme. This enables us to cost-effectively reconstruct high-resolution EV data using only low-resolution measurements. Simulation results using real industrial data reveal the excellent performance of the proposed methods.
Enforcing C/C++ Type and Scope at Runtime for Control-Flow and Data-Flow Integrity
Ismail, Mohannad; Jelesnianski, Christopher; Jang, Yeongjin; Min, Changwoo; Xiong, Wenjie (ACM, 2024-04-27)
Control-flow hijacking and data-oriented attacks are becoming more sophisticated. These attacks, especially dataoriented attacks, can result in critical security threats, such as leaking an SSL key. Data-oriented attacks are hard to defend against with acceptable performance due to the sheer amount of data pointers present. The root cause of such attacks is using pointers in unintended ways; fundamentally, these attacks rely on abusing pointers to violate the original scope they were used in or the original types that they were declared as. This paper proposes Scope Type Integrity (STI), a new defense policy that enforces all pointers (both code and data pointers) to conform to the original programmer’s intent, as well as Runtime Scope Type Integrity (RSTI) mechanisms to enforce STI at runtime leveraging ARM Pointer Authentication. STI gathers information about the scope, type, and permissions of pointers. This information is then leveraged by RSTI to ensure pointers are legitimately utilized at runtime. We implemented three defense mechanisms of RSTI, with varying levels of security and performance tradeoffs to showcase the versatility of RSTI. We employ these three variants on a variety of benchmarks and real-world applications for a full security and performance evaluation of these mechanisms. Our results show that they have overheads of 5.29%, 2.97%, and 11.12%, respectively.
Designing Technology to Support the Hospital Classroom: Preliminary Findings
Rasberry, Nadra; Essandoh, Joshua; Do, Ethan; Ogbonnaya-Ogburu, Ihudiya (ACM, 2024-11-11)
Hospital teachers are state-employed educators who provide K-12 instruction to children in the hospital. We conducted research to understand how technology is used in hospital classrooms, an area which has been relatively underexplored. We conducted semistructured interviews with five hospital teachers to understand their experience of using technology in and outside the classroom. Our findings revealed that hospital teachers often rely on older curricula given the changing education atmosphere; learning is often assessed through in-classroom observations of mastery; and technology and internet use by students is often restricted, which may inhibit opportunities to use AI and other technical resources in the classroom.We contribute a deeper understanding of technology use in the hospital classroom.
Practical Fault Injection Attacks on Constant Time CSIDH and Mitigation Techniques
Chiu, Tinghung; LeGrow, Jason; Xiong, Wenjie (ACM, 2024-11-19)
Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) is an isogeny-based key exchange protocol which is believed to be secure even when parties use long-lived secret keys. To secure CSIDH against side-channel attacks, constant-time implementations with additional dummy isogeny computations are employed. In this study, we demonstrate a fault injection attack on the constant-time real-then-dummy CSIDH to recover the full static secret key. We prototype the attack using voltage glitches on the victim STM32 microcontroller. The attack scheme, which is based on existing research which has yet to be practically implemented, involves getting the faulty output by injecting the fault in a binary search fashion. Our attack reveals many practical factors that were not considered in the previous theoretical fault injection attack analysis, e.g., the probability of a failed fault injection. We bring the practice to theory and developed new complexity analysis of the attack. Further, to mitigate the possible binary search attack on real-then-dummy CSIDH, dynamic random vector CSIDH was proposed previously to randomize the order of real and dummy isogeny operations. We explore fault injection attacks on dynamic random vector CSIDH and evaluate the security level of the mitigation. Our analysis and experimental results demonstrate that it is infeasible to attack dynamic random vector CSIDH in a reasonable amount of time when the success rate of fault injection is not consistent over time.
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.

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