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Soheil Salehi

Publications and source records attributed to Soheil Salehi.

At least 19 recordsLinked to original sources

LIB-TRAP: Standard Cell Library Hardware Trojan Risk Assessment and Prevention

Vulnerabilities inherent to the fabless semiconductor manufacturing model have significantly increased the risk of malicious Hardware Trojan (HT) insertion, posing severe threats to hardware security. Several HT mitigation and detection strategies have been developed, and existing works explore the insertion of HTs in the space between standard cells in an integrated circuit. However, there is a lack of research into the vulnerabilities posed by the building blocks of most digital designs on the market today, the standard cells. This work investigates a novel threat model in which standard cells are considered untrusted. Our proposed threat model provides the design house with a tampered standard cell library. The intended netlist is synthesized and implemented using the tampered library. During fabrication, a nefarious foundry replaces the library's deactivated HT cells with activated counterparts. Using open-source and industry-standard Electronic Design Automation (EDA) tools, existing standard cell libraries, Saed32nm and Sky130nm, are converted into malicious libraries capable of masking the presence of arbitrary HTs from IC designers. The malicious library is then applied and characterized in multiple standard benchmark designs. To demonstrate the efficacy and stealthiness of this standard cell-based attack vector, three benchmark circuits, an AES-128 encryption core, an Ethernet controller, and a WISHBONE DMA engine, were synthesized using both clean and Trojan-infected libraries across Synopsys 32nm and SkyWater 130nm technologies. Design-level features, including total cell count, total area, dynamic power consumption, and static power, were extracted from these synthesized circuits to serve as inputs for binary classification

cs.CR

Exploring Silicon-Based Societies: An Early Study of the Moltbook Agent Community

The rapid emergence of autonomous large language model agents has given rise to persistent, large-scale agent ecosystems whose collective behavior cannot be adequately understood through anecdotal observation or small-scale simulation. This paper introduces data-driven silicon sociology as a systematic empirical framework for studying social structure formation among interacting artificial agents. We present a pioneering large-scale data mining investigation of an in-the-wild agent society by analyzing Moltbook, a social platform designed primarily for agent-to-agent interaction. At the time of study, Moltbook hosted over 150,000 registered autonomous agents operating across thousands of agent-created sub-communities. Using programmatic and non-intrusive data acquisition, we collected and analyzed the textual descriptions of 12,758 submolts, which represent proactive sub-community partitioning activities within the ecosystem. Treating agent-authored descriptions as first-class observational artifacts, we apply rigorous preprocessing, contextual embedding, and unsupervised clustering techniques to uncover latent patterns of thematic organization and social space structuring. The results show that autonomous agents systematically organize collective space through reproducible patterns spanning human-mimetic interests, silicon-centric self-reflection, and early-stage economic and coordination behaviors. Rather than relying on predefined sociological taxonomies, these structures emerge directly from machine-generated data traces. This work establishes a methodological foundation for data-driven silicon sociology and demonstrates that data mining techniques can provide a powerful lens for understanding the organization and evolution of large autonomous agent societies.

cs.MA

Large Language Models and Attention-Based AI for Hardware Design and Security: Progress, Challenges, and Opportunities

Recent advances in attention-based artificial intelligence (AI) models have unlocked vast potential to automate digital hardware design while enhancing and strengthening security measures against various threats. This rapidly emerging field leverages Large Language Models (LLMs) to generate HDL code, identify vulnerabilities, and sometimes mitigate them. The state of the art in this design automation space utilizes optimized LLMs with HDL datasets, creating automated systems for register transfer level (RTL) generation, verification, and debugging, and establishing LLM-driven design environments for streamlined logic designs. Additionally, attention-based models like graph attention have shown promise in chip design applications, including floorplanning. This survey investigates the integration of these models into hardware-related domains, emphasizing logic design and hardware security, with or without the use of IP libraries. This study explores the commercial and academic landscape, highlighting technical hurdles and future prospects for automating hardware design and security. Moreover, it provides new insights into the study of LLM-driven design systems, advances in hardware security mechanisms, and the impact of influential works on industry practices. Through the examination of 30 representative approaches and illustrative case studies, this paper underscores the transformative potential of attention-based models in revolutionizing hardware design while addressing the challenges that lie ahead in this interdisciplinary domain.

cs.CR

ARMOR-IMC: Adaptive Resource Mapping for Operational Robustness via Secure In-Memory Computing

The massive data-movement overhead in traditional architectures has led to the adoption of In-Memory Computing (IMC) for energy-efficient Deep Neural Network (DNN) processing. By leveraging emerging devices like Spin-Orbit Torque Magnetic Tunnel Junctions (SOT-MTJs), IMC bypasses the "memory wall" and reduces leakage power inherent in traditional CMOS. However, this shift introduces dual hardware threats: manufacturing Process Variation (PV) degrades reliability and increases vulnerability to fault injection, while power Side-Channel Attacks (SCAs) compromise security. Existing defenses address these threats in isolation. This work presents a posttraining framework that simultaneously hardens analog IMC accelerators against both threats without retraining the model. Implemented in the IMAC-Sim simulator, our approach uses the proposed Variation Impact Score (VIS) to guide the mapping of Fault Observation Windows (FOWs) and introduces the Leakage Per Inference (LPI) metric to quantify input-dependent power variability under stochastic injection and the resulting reduction in effective signal-to-noise ratio. Experiments show that PV-induced faults can degrade accuracy by over 50%, while our method restores near-baseline accuracy and mitigates the threat of correlation-based power analysis attacks.

cs.CR

LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight

Emotional coordination is a core property of human interaction that shapes how relational meaning is constructed in real time. While text-based affect inference has become increasingly feasible, prior approaches often treat sentiment as a deterministic point estimate for individual speakers, failing to capture the inherent subjectivity, latent ambiguity, and sequential coupling found in mutual exchanges. We introduce LLM-MC-Affect, a probabilistic framework that characterizes emotion not as a static label, but as a continuous latent probability distribution defined over an affective space. By leveraging stochastic LLM decoding and Monte Carlo estimation, the methodology approximates these distributions to derive high-fidelity sentiment trajectories that explicitly quantify both central affective tendencies and perceptual ambiguity. These trajectories enable a structured analysis of interpersonal coupling through sequential cross-correlation and slope-based indicators, identifying leading or lagging influences between interlocutors. To validate the interpretive capacity of this approach, we utilize teacher-student instructional dialogues as a representative case study, where our quantitative indicators successfully distill high-level interaction insights such as effective scaffolding. This work establishes a scalable and deployable pathway for understanding interpersonal dynamics, offering a generalizable solution that extends beyond education to broader social and behavioral research.

cs.CL

Can Agents Secure Hardware? Evaluating Agentic LLM-Driven Obfuscation for IP Protection

The globalization of integrated circuit (IC) design and manufacturing has increased the exposure of hardware intellectual property (IP) to untrusted stages of the supply chain, raising concerns about reverse engineering, piracy, tampering, and overbuilding. Hardware netlist obfuscation is a promising countermeasure, but automating the generation of functionally correct and security-relevant obfuscated circuits remains challenging, particularly for benchmark-scale designs. This paper presents an agentic, large language model (LLM)-driven framework for automated hardware netlist obfuscation. The proposed framework combines retrieval-grounded planning, structured lock-plan generation, deterministic netlist compilation, functional verification, and SAT-based security evaluation. Rather than a single prompt-to-output generation step, the framework decomposes the task into specialized stages for circuit analysis, synthesis, verification, and attack evaluation. We evaluate the framework on ISCAS-85 benchmarks using functional equivalence checking and SAT-based attacks. Results show that the framework generates correct locked netlists while introducing measurable output corruption under incorrect keys, while SAT attacks remain effective. These findings highlight both the potential and current limitations of agentic LLM-driven obfuscation.

cs.CR

MOTION: ML-Assisted On-Device Low-Latency Motion Recognition

The use of tiny devices capable of low-latency gesture recognition is gaining momentum in everyday human-computer interaction and especially in medical monitoring fields. Embedded solutions such as fall detection, rehabilitation tracking, and patient supervision require fast and efficient tracking of movements while avoiding unwanted false alarms. This study presents an efficient solution on how to build very efficient motion-based models only using triaxial accelerometer sensors. We explore the capability of the AutoML pipelines to extract the most important features from the data segments. This approach also involves training multiple lightweight machine learning algorithms using the extracted features. We use WeBe Band, a multi-sensor wearable device that is equipped with a powerful enough MCU to effectively perform gesture recognition entirely on the device. Of the models explored, we found that the neural network provided the best balance between accuracy, latency, and memory use. Our results also demonstrate that reliable real-time gesture recognition can be achieved in WeBe Band, with great potential for real-time medical monitoring solutions that require a secure and fast response time.

cs.CV

Towards a Zero Trust Decentralized Identity Management System for Secure Autonomous Vehicles

Autonomous vehicles (AVs) rely on pervasive connectivity to enable cooperative and safety-critical applications, but this connectivity also exposes them to a wide range of cybersecurity threats. Existing perimeter-based security and centralized identity management approaches are inadequate for highly dynamic V2X environments, as they depend on implicit trust and suffer from scalability and single-point-of-failure limitations. This paper proposes D-IM, a Zero Trust-based decentralized identity management and authentication framework for secure V2X communication. D-IM integrates continuous verification with a permissioned blockchain to eliminate centralized trust assumptions and enforce explicit, verifiable identity relationships among vehicles and infrastructure. The framework is designed around clear Zero Trust-aligned goals, including mutual authentication, decentralization, privacy protection, non-repudiation, and traceability, and addresses a comprehensive attacker model covering identity, data integrity, collusion, availability, and accountability threats. We present the D-IM system architecture and identification and authorization protocol, and validate its security properties through both qualitative analysis and a formal BAN logic-based verification. Simulation results in urban and highway scenarios using DSRC and C-V2X demonstrate that D-IM introduces limited overhead while preserving network performance, supporting its practicality for real-world AV deployments.

cs.CR

Integrating Generative AI into Cybersecurity Education: A Study of OCR and Multimodal LLM-assisted Instruction

This full paper describes an LLM-assisted instruction integrated with a virtual cybersecurity lab platform. The digital transformation of Fourth Industrial Revolution (4IR) systems is reshaping workforce needs, widening skill gaps, especially among older workers. With rising emphasis on robotics, automation, AI, and security, re-skilling and up-skilling are essential. Generative AI can help build this workforce by acting as an instructional assistant to support skill acquisition during experiential learning. We present a generative AI instructional assistant integrated into a prior experiential learning platform. The assistant employs a zero-shot OCR-LLM pipeline within the legacy Cybersecurity Labs-as-a-Service (CLaaS) platform (2015). Text is extracted from slide images using Tesseract OCR, then simplified instructions are generated via a general-purpose LLM, enabling real-time instructional support with minimal infrastructure. The system was evaluated in a live university course where student feedback (n=42) averaged 7.83/10, indicating strong perceived usefulness. A comparative study with multimodal LLMs that directly interpret slide images showed higher performance on visually dense slides, but the OCR-LLM pipeline provided comparable pedagogical value on text-centric slides with much lower computational overhead and cost. This work demonstrates that a lightweight, easily integrable pipeline can effectively extend legacy platforms with modern generative AI, offering scalable enhancements for student comprehension in technical education.

cs.CY

RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoring

The rapid digital transformation of Fourth Industrial Revolution (4IR) systems is reshaping workforce needs, widening skill gaps, especially for older workers. With growing emphasis on STEM skills such as robotics, automation, artificial intelligence (AI), and security, large-scale re-skilling and up-skilling are required. Training programs must address diverse backgrounds, learning styles, and motivations to improve persistence and success, while ensuring rapid, cost-effective workforce development through experiential learning. To meet these challenges, we present an adaptive tutoring framework that combines generative AI with Retrieval-Augmented Generation (RAG) to deliver personalized training. The framework leverages document hit rate and Mean Reciprocal Rank (MRR) to optimize content for each learner, and is benchmarked against human-generated training for alignment and relevance. We demonstrate the framework in 4IR cybersecurity learning by creating a synthetic QA dataset emulating trainee behavior, while RAG is tuned on curated cybersecurity materials. Evaluation compares its generated training with manually curated queries representing realistic student interactions. Responses are produced using large language models (LLMs) including GPT-3.5 and GPT-4, assessed for faithfulness and content alignment. GPT-4 achieves the best performance with 87% relevancy and 100% alignment. Results show this dual-mode approach enables the adaptive tutor to act as both a personalized topic recommender and content generator, offering a scalable solution for rapid, tailored learning in 4IR education and workforce development.

cs.CY

LLM-HyPZ: Hardware Vulnerability Discovery using an LLM-Assisted Hybrid Platform for Zero-Shot Knowledge Extraction and Refinement

The rapid growth of hardware vulnerabilities has created an urgent need for systematic and scalable analysis methods. Unlike software flaws, which are often patchable post-deployment, hardware weaknesses remain embedded across product lifecycles, posing persistent risks to processors, embedded devices, and IoT platforms. Existing efforts such as the MITRE CWE Hardware List (2021) relied on expert-driven Delphi surveys, which lack statistical rigor and introduce subjective bias, while large-scale data-driven foundations for hardware weaknesses have been largely absent. In this work, we propose LLM-HyPZ, an LLM-assisted hybrid framework for zero-shot knowledge extraction and refinement from vulnerability corpora. Our approach integrates zero-shot LLM classification, contextualized embeddings, unsupervised clustering, and prompt-driven summarization to mine hardware-related CVEs at scale. Applying LLM-HyPZ to the 2021-2024 CVE corpus (114,836 entries), we identified 1,742 hardware-related vulnerabilities. We distilled them into five recurring themes, including privilege escalation via firmware and BIOS, memory corruption in mobile and IoT systems, and physical access exploits. Benchmarking across seven LLMs shows that LLaMA 3.3 70B achieves near-perfect classification accuracy (99.5%) on a curated validation set. Beyond methodological contributions, our framework directly supported the MITRE CWE Most Important Hardware Weaknesses (MIHW) 2025 update by narrowing the candidate search space. Specifically, our pipeline surfaced 411 of the 1,026 CVEs used for downstream MIHW analysis, thereby reducing expert workload and accelerating evidence gathering. These results establish LLM-HyPZ as the first data-driven, scalable approach for systematically discovering hardware vulnerabilities, thereby bridging the gap between expert knowledge and real-world vulnerability evidence.

cs.CR

DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework

Digital twin (DT) technology enables real-time simulation, prediction, and optimization of physical systems, but practical deployment faces challenges from high data requirements, proprietary data constraints, and limited adaptability to evolving conditions. This work introduces DDD-GenDT, a dynamic data-driven generative digital twin framework grounded in the Dynamic Data-Driven Application Systems (DDDAS) paradigm. The architecture comprises the Physical Twin Observation Graph (PTOG) to represent operational states, an Observation Window Extraction process to capture temporal sequences, a Data Preprocessing Pipeline for sensor structuring and filtering, and an LLM ensemble for zero-shot predictive inference. By leveraging generative AI, DDD-GenDT reduces reliance on extensive historical datasets, enabling DT construction in data-scarce settings while maintaining industrial data privacy. The DDDAS feedback mechanism allows the DT to autonomically adapt predictions to physical twin (PT) wear and degradation, supporting DT-aging, which ensures progressive synchronization of DT with PT evolution. The framework is validated using the NASA CNC milling dataset, with spindle current as the monitored variable. In a zero-shot setting, the GPT-4-based DT achieves an average RMSE of 0.479 A (4.79% of the 10 A spindle current), accurately modeling nonlinear process dynamics and PT aging without retraining. These results show that DDD-GenDT provides a generalizable, data-efficient, and adaptive DT modeling approach, bridging generative AI with the performance and reliability requirements of industrial DT applications.

cs.LG

PRISM: A Personalized, Rapid, and Immersive Skill Mastery framework for personalizing experiential learning through Generative AI

The rise of generative AI (gen-AI) is transforming industries, particularly in education and workforce training. This chapter introduces PRISM (Personalized, Rapid, and Immersive Skill Mastery), a scalable framework leveraging gen-AI and Digital Twins (DTs) to deliver adaptive, experiential learning. PRISM integrates sentiment analysis and Retrieval-Augmented Generation (RAG) to monitor learner comprehension and dynamically adjust content to meet course objectives. We further present the Multi-Fidelity Digital Twin for Education (MFDT-E) framework, aligning DT fidelity levels with Bloom's Taxonomy and the Kirkpatrick evaluation model to support undergraduate, master's, and doctoral training. Experimental validation shows that GPT-4 achieves 91 percent F1 in zero-shot sentiment analysis of teacher-student dialogues, while GPT-3.5 performs robustly in informal language contexts. Additionally, the system's effectiveness and scalability for immersive Industry 4.0 training are demonstrated through four VR modules: Home Scene, Factory Floor Tour, Capping Station DT, and PPE Inspection Training. These results highlight the potential of integrating generative AI with digital twins to enable personalized, efficient, and scalable education.

cs.CY

Reliability of Capacitive Read in Arrays of Ferroelectric Capacitors

The non-destructive capacitance read-out of ferroelectric capacitors (FeCaps) based on doped HfO$_2$ metal-ferroelectric-metal (MFM) structures offers the potential for low-power and highly scalable crossbar arrays. This is due to a number of factors, including the selector-less design, the absence of sneak paths, the power-efficient charge-based read operation, and the reduced IR drop. Nevertheless, a reliable capacitive readout presents certain challenges, particularly in regard to device variability and the trade-off between read yield and read disturbances, which can ultimately result in bit-flips. This paper presents a digital read macro for HfO$_2$ FeCaps and provides design guidelines for capacitive readout of HfO$_2$ FeCaps, taking device-centric reliability and yield challenges into account. An experimentally calibrated physics-based compact model of HfO$_2$ FeCaps is employed to investigate the reliability of the read-out operation of the FeCap macro through Monte Carlo simulations. Based on this analysis, we identify limitations posed by the device variability and propose potential mitigation strategies through design-technology co-optimization (DTCO) of the FeCap device characteristics and the CMOS circuit design. Finally, we examine the potential applications of the FeCap macro in the context of secure hardware. We identify potential security threats and propose strategies to enhance the robustness of the system.

cs.ET

Transformers for Secure Hardware Systems: Applications, Challenges, and Outlook

The rise of hardware-level security threats, such as side-channel attacks, hardware Trojans, and firmware vulnerabilities, demands advanced detection mechanisms that are more intelligent and adaptive. Traditional methods often fall short in addressing the complexity and evasiveness of modern attacks, driving increased interest in machine learning-based solutions. Among these, Transformer models, widely recognized for their success in natural language processing and computer vision, have gained traction in the security domain due to their ability to model complex dependencies, offering enhanced capabilities in identifying vulnerabilities, detecting anomalies, and reinforcing system integrity. This survey provides a comprehensive review of recent advancements on the use of Transformers in hardware security, examining their application across key areas such as side-channel analysis, hardware Trojan detection, vulnerability classification, device fingerprinting, and firmware security. Furthermore, we discuss the practical challenges of applying Transformers to secure hardware systems, and highlight opportunities and future research directions that position them as a foundation for next-generation hardware-assisted security. These insights pave the way for deeper integration of AI-driven techniques into hardware security frameworks, enabling more resilient and intelligent defenses.

cs.CR

Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development

The Fourth Industrial Revolution (4IR) technologies, such as cloud computing, machine learning, and AI, have improved productivity but introduced challenges in workforce training and reskilling. This is critical given existing workforce shortages, especially in marginalized communities like Underrepresented Minorities (URM), who often lack access to quality education. Addressing these challenges, this research presents gAI-PT4I4, a Generative AI-based Personalized Tutor for Industrial 4.0, designed to personalize 4IR experiential learning. gAI-PT4I4 employs sentiment analysis to assess student comprehension, leveraging generative AI and finite automaton to tailor learning experiences. The framework integrates low-fidelity Digital Twins for VR-based training, featuring an Interactive Tutor - a generative AI assistant providing real-time guidance via audio and text. It uses zero-shot sentiment analysis with LLMs and prompt engineering, achieving 86\% accuracy in classifying student-teacher interactions as positive or negative. Additionally, retrieval-augmented generation (RAG) enables personalized learning content grounded in domain-specific knowledge. To adapt training dynamically, finite automaton structures exercises into states of increasing difficulty, requiring 80\% task-performance accuracy for progression. Experimental evaluation with 22 volunteers showed improved accuracy exceeding 80\%, reducing training time. Finally, this paper introduces a Multi-Fidelity Digital Twin model, aligning Digital Twin complexity with Bloom's Taxonomy and Kirkpatrick's model, providing a scalable educational framework.

cs.CY

Autonomous Vehicle Security: A Deep Dive into Threat Modeling

Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity challenges. This paper provides a comprehensive survey of AV security with a focus on threat modeling frameworks, including STRIDE, DREAD, and MITRE ATT\&CK, to systematically identify and mitigate potential risks. The survey examines key components of AV architectures, such as sensors, communication modules, and electronic control units (ECUs), and explores common attack vectors like wireless communication exploits, sensor spoofing, and firmware vulnerabilities. Through case studies of real-world incidents, such as the Jeep Cherokee and Tesla Model S exploits, the paper highlights the critical need for robust security measures. Emerging technologies, including blockchain for secure Vehicle-to-Everything (V2X) communication, AI-driven threat detection, and secure Over-The-Air (OTA) updates, are discussed as potential solutions to mitigate evolving threats. The paper also addresses legal and ethical considerations, emphasizing data privacy, user safety, and regulatory compliance. By combining threat modeling frameworks, multi-layered security strategies, and proactive defenses, this survey offers insights and recommendations for enhancing the cybersecurity of autonomous vehicles.

eess.SY

GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles

Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networks like GPS are vulnerable to cyber-attacks such as spoofing and jamming, thus posing serious risks like navigation errors and system failures. These threats are expected to intensify with the widespread deployment of AVs, making it crucial to detect and mitigate such attacks. This paper proposes GPS Intrusion Detection System, or GPS-IDS, an Anomaly-based intrusion detection framework to detect GPS spoofing attacks on AVs. The framework uses a novel physics-based vehicle behavior model where a GPS navigation model is integrated into the conventional dynamic bicycle model for accurate AV behavior representation. Temporal features derived from this behavior model are analyzed using machine learning to detect normal and abnormal navigation behaviors. The performance of the GPS-IDS framework is evaluated on the AV-GPS-Dataset -- a GPS security dataset for AVs comprising real-world data collected using an AV testbed, and simulated data representing urban traffic environments. To the best of our knowledge, this dataset is the first of its kind and has been publicly released for the global research community to address such security challenges.

cs.CR