arXiv subjects

Norrathep Rattanavipanon

Publications and source records attributed to Norrathep Rattanavipanon.

At least 19 recordsLinked to original sources

arXiv2026

Resolving Conflicts Between RTOS Timekeeping and Uninterruptable Trusted Computing

Trusted Execution Environments (TEEs) on low-power microcontrollers (e.g., ARM TrustZone-M) enable isolation of Secure and Non-Secure software but still require both worlds to share resources, including interrupt controllers. In this model, real-time applications and real-time operating systems (RTOS-s) are executed in the Non-Secure sub-system, whereas the Secure sub-system is typically reserved for a small set of pre-defined security (e.g., cryptographic) operations referred to as trusted computing services. However, many RTOS-s rely on periodic interrupts (SysTicks) to advance their own notion of time (time-keeping), and the delivery of this interrupt is essential for preserving real-time behavior. On the other hand, the security of many trusted computing services requires atomicity vis-a-vis the Non-Secure sub-system (where the RTOS resides), precluding SysTick handling. This paper first characterizes this conflict and then introduces a Secure-driven time synchronization mechanism in which the Secure World measures elapsed time and compensates the Non-Secure RTOS by unobtrusively updating the RTOS time-keeping data structures with the appropriate number of missed ticks before re-enabling interrupts and resuming the execution of the Non-Secure system. This approach restores a consistent, monotonic notion of time across worlds and enables secure coexistence of trusted computing services and RTOS-s on microcontrollers. Importantly, the proposed approach requires no modifications to the underlying RTOS and yields no significant run-time overhead.

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arXiv2026

MOAT: Model-Agnostic Randomized Transformations for preventing Efficiency Degradation Attacks on ViTs

To adopt the Vision Transformers (ViTs) in resource-constrained environment, token pruning is widely used to reduce computational cost without impacting accuracy. However, adversaries have developed targeted attacks against said token pruning techniques to undermine such attempts to make ViTs efficient. In this paper, we propose MOAT, a model-agnostic pre-processing defense pipeline that applies a combination of input transformations to protect efficient ViT implementations against adversarial efficiency attacks. MOAT operates directly on the input without requiring modifications to the model architecture or token pruning mechanism. Experimental results demonstrate that, across all evaluated ViT models, MOAT limits GFLOPs degradation under adversarial attacks to within 3.4% of the original unattacked model.

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arXiv2026

SMARM+: Analyzing and Enhancing Shuffled Measurements for Remote Attestation in Real-Time IoT Settings

Remote attestation (RA) is a lightweight security primitive for detecting software compromise on IoT devices. Traditional RA schemes require atomic, non-interruptible memory measurements, making them difficult to deploy alongside real-time workloads. SMARM addresses this limitation by measuring memory in a secret, shuffled block order, reducing the non-interruptibility period to the duration of a single block measurement. However, SMARM was originally designed for microkernel-based systems and has not been studied in RTOS-driven real-time environments. In this work, we present the first systematic study of SMARM in real-time RTOS-based setups. We implement SMARM on commodity ARM TrustZone-M hardware running FreeRTOS and Zephyr, and introduce the Frequency Accuracy Ratio (FAR) to quantify the extent to which attestation can coexist with real-time execution under varying workloads. Our evaluation shows that SMARM's real-time compatibility is highly sensitive to block size: large blocks significantly degrade real-time availability, while small blocks incur substantial secure-storage overhead, limiting deployability on memory-constrained devices. To address this limitation, we propose SMARM+, a family of enhanced SMARM variants consisting of SMARM+PRNG and SMARM+FPE. They are designed to reduce secure-storage requirements while preserving SMARM's security guarantees and real-time behavior. Our evaluation highlights the trade-off between secure-storage reduction, attestation runtime, and energy overhead, and provides guidance on selecting among SMARM, SMARM+PRNG, and SMARM+FPE for different deployment settings.

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arXiv2026

Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning

Privacy-preserving Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models without exposing raw data, but exchanged model updates remain vulnerable to inference attacks from honest-but-curious servers. Homomorphic Encryption (HE) addresses this by allowing server-side aggregation directly on encrypted updates, with the CKKS scheme being particularly suitable due to its native support for approximate floating-point arithmetic. However, no prior work has examined how to configure CKKS for PFL deployments, leaving practitioners without principled guidance on parameter selection that directly affects privacy, precision, and computational cost. This paper presents pFedCKKS, a generic framework integrating CKKS into PFL, and provides the first systematic parameter selection guide for practitioners. We derive the full CKKS parameter constraints under 128-bit security for the PFL setting, showing the selection problem reduces to choosing just two values: the inner and outer ciphertext prime. Implemented using the Flower framework and TenSEAL library, pFedCKKS is evaluated on the FEMNIST, CelebA and Sentiment140 datasets with FedFinetune, Ditto and FedPer which represents PFL algorithms. Experimental results reveal an empirical trade-off between precision and computational/communication costs. This allows us to draw a concrete guideline for selecting proper CKKS parameters that balance efficiency and accuracy in real-world deployments of pFedCKKS.

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arXiv2026

FDM: A Framework for Decision-making to build ML-based Malware detection systems

Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts. This paper proposes the Framework for Decision-making (FDM) to build ML-based malware detection systems. The FDM formalises this selection process using the Weighted Configuration Compatibility Score (WCCS), a multi-criteria scoring function mapping five operational parameters (platform constraint, resource budget, response latency, update frequency, and detection sensitivity) to ranked recommendations across nine configuration dimensions. To validate the framework, four experiments were conducted on three datasets (a private Windows API dataset, the public Malimg image benchmark, and an Android static API dataset). Key results include: (i) XGBoost achieved the best accuracy-to-resource ratio in binary classification (97.46 % test accuracy, <70 MB RAM), outperforming LSTM/BiLSTM which consumed up to 2.8 GB; (ii) in multi-class classification, classical models (XGBoost 79.03 %) outperformed recurrent deep models (BiLSTM 72.27 %), reversing the binary ranking; (iii) class-incremental learning with EfficientNetB0 maintained 99.13 % accuracy with only 0.65 pp degradation across 11 incremental steps; (iv) transfer learning reduced training time by 2.14 times on average for image-based malware data without significant accuracy cost; and (v) autoencoder pre-processing yielded a 14 times training speedup at a cost of only 0.86 pp accuracy. These findings confirm that the optimal ML configuration is context-dependent, validating the FDM's core premise and demonstrating its practical utility for cybersecurity practitioners.

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arXiv2026

Verifiable and Confidential DNN Inference on Low-End Edge Devices

Deploying deep neural network (DNN) inference on low-end edge devices raises two key challenges: protecting model confidentiality against a potentially compromised edge system and enabling verifiable inference without incurring prohibitive overhead. Existing approaches either house partial models and inference software within trusted execution environments (TEEs), resulting in high cost and an application-dependent trusted computing base (TCB), or execute in untrusted environments, providing little security. In this work, we present VECODI, a framework for verifiable and confidential DNN inference on constrained edge devices. At its core, VECODI introduces SHANGRI-LA, a new execution abstraction on TrustZone-M TEEs that establishes a third runtime environment with privileges strictly between the Secure and Non-Secure Worlds. VECODI leverages SHANGRI-LA to execute untrusted inference code in the Non-Secure World while using minimal application-agnostic Secure-World support to protect model confidentiality and enable verifiability (with respect to proper execution of inference code and model parameters) of inference results. We realize VECODI on a real-world NUCLEO-L552ZE-Q development board and open-source its prototype. Our results show VECODI's small TCB, memory footprint, and runtime overhead, making it a practical option for secure inference in low-end edge devices.

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arXiv2026

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning

Federated learning (FL) enables collaborative training without raw data sharing, but still risks training data memorization. Existing FL memorization detection techniques focus on one sample at a time, underestimating more subtle risks of cross-sample memorization. In contrast, recent work on centralized learning (CL) has introduced fine-grained methods to assess memorization across all samples in training data, but these assume centralized access to data and cannot be applied directly to FL. We bridge this gap by proposing a framework that quantifies both intra- and inter-client memorization in FL using fine-grained cross-sample memorization measurement across all clients. Based on this framework, we conduct two studies: (1) measuring subtle memorization across clients and (2) examining key factors that influence memorization, including decoding strategies, prefix length, and FL algorithms. Our findings reveal that FL models do memorize client data, particularly intra-client data, more than inter-client data, with memorization influenced by training and inferencing factors.

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arXiv2025

On the Verification of Control Flow Attestation Evidence

Remote run-time attestation methods, including Control Flow Attestation (CFA) and Data Flow Attestation (DFA), have been proposed to generate precise evidence of execution's control flow path (in CFA) and optionally execution data inputs (in DFA) on a remote and potentially compromised embedded device, hereby referred to as a Prover (Prv). Recent advances in run-time attestation architectures are also able to guarantee that a remote Verifier (Vrf) reliably receives this evidence from Prv, even when Prv's software state is fully compromised. This, in theory, enables secure "run-time auditing" in addition to best-effort attestation, i.e., it guarantees that Vrf can examine execution evidence to identify previously unknown compromises as soon as they are exploited, pinpoint their root cause(s), and remediate them. However, prior work has for the most part focused on securely implementing Prv's root of trust (responsible for generating authentic run-time evidence), leaving Vrf 's perspective in this security service unexplored. In this work, we argue that run-time attestation and auditing are only truly useful if Vrf can effectively analyze received evidence. From this premise, we characterize different types of evidence produced by existing run-time attestation/auditing architectures in terms of Vrf 's ability to detect and remediate (previously unknown) vulnerabilities. As a case study for practical uses of run-time evidence by Vrf, we propose SABRE: a Security Analysis and Binary Repair Engine. SABRE showcases how Vrf can systematically leverage run-time evidence to detect control flow attacks, pinpoint corrupted control data and specific instructions used to corrupt them, and leverage this evidence to automatically generate binary patches to buffer overflow and use-after-free vulnerabilities without source code knowledge.

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arXiv2025

Provable Execution in Real-Time Embedded Systems

Embedded devices are increasingly ubiquitous and vital, often supporting safety-critical functions. However, due to strict cost and energy constraints, they are typically implemented with Micro-Controller Units (MCUs) that lack advanced architectural security features. Within this space, recent efforts have created low-cost architectures capable of generating Proofs of Execution (PoX) of software on potentially compromised MCUs. This capability can ensure the integrity of sensor data from the outset, by binding sensed results to an unforgeable cryptographic proof of execution on edge sensor MCUs. However, the security of existing PoX requires the proven execution to occur atomically. This requirement precludes the application of PoX to (1) time-shared systems, and (2) applications with real-time constraints, creating a direct conflict between execution integrity and the real-time availability needs of several embedded system uses. In this paper, we formulate a new security goal called Real-Time Proof of Execution (RT-PoX) that retains the integrity guarantees of classic PoX while enabling its application to existing real-time systems. This is achieved by relaxing the atomicity requirement of PoX while dispatching interference attempts from other potentially malicious tasks (or compromised operating systems) executing on the same device. To realize the RT-PoX goal, we develop Provable Execution Architecture for Real-Time Systems (PEARTS). To the best of our knowledge, PEARTS is the first PoX system that can be directly deployed alongside a commodity embedded real-time operating system (FreeRTOS). This enables both real-time scheduling and execution integrity guarantees on commodity MCUs. To showcase this capability, we develop a PEARTS open-source prototype atop FreeRTOS on a single-core ARM Cortex-M33 processor. We evaluate and report on PEARTS security and (modest) overheads.

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arXiv2025

Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution

The rise in IoT-driven distributed data analytics, coupled with increasing privacy concerns, has led to a demand for effective privacy-preserving and federated data collection/model training mechanisms. In response, approaches such as Federated Learning (FL) and Local Differential Privacy (LDP) have been proposed and attracted much attention over the past few years. However, they still share the common limitation of being vulnerable to poisoning attacks wherein adversaries compromising edge devices feed forged (a.k.a. poisoned) data to aggregation back-ends, undermining the integrity of FL/LDP results. In this work, we propose a system-level approach to remedy this issue based on a novel security notion of Proofs of Stateful Execution (PoSX) for IoT/embedded devices' software. To realize the PoSX concept, we design SLAPP: a System-Level Approach for Poisoning Prevention. SLAPP leverages commodity security features of embedded devices - in particular ARM TrustZoneM security extensions - to verifiably bind raw sensed data to their correct usage as part of FL/LDP edge device routines. As a consequence, it offers robust security guarantees against poisoning. Our evaluation, based on real-world prototypes featuring multiple cryptographic primitives and data collection schemes, showcases SLAPP's security and low overhead.

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arXiv2024

$π$QLB: A Privacy-preserving with Integrity-assuring Query Language for Blockchain

The increase in the adoption of blockchain technology in different application domains e.g., healthcare systems, supplychain management, has raised the demand for a data query mechanism on blockchain. Since current blockchain systems lack the support for querying data with embedded security and privacy guarantees, there exists inherent security and privacy concerns on those systems. In particular, existing systems require users to submit queries to blockchain operators (e.g., a node validator) in plaintext. This directly jeopardizes users' privacy as the submitted queries may contain sensitive information, e.g., location or gender preferences, that the users may not be comfortable sharing. On the other hand, currently, the only way for users to ensure integrity of the query result is to maintain the entire blockchain database and perform the queries locally. Doing so incurs high storage and computational costs on the users, precluding this approach to be practically deployable on common light-weight devices (e.g., smartphones). To this end, this paper proposes $π$QLB, a query language for blockchain systems that ensures both confidentiality of query inputs and integrity of query results. Additionally, $π$QLB enables SQL-like queries over the blockchain data by introducing relational data semantics into the existing blockchain database. $π$QLB has applied the recent cryptography primitive, i.e., function secret sharing (FSS), to achieve confidentiality. To support integrity, we extend the traditional FSS setting in such a way that integrity of FSS results can be efficiently verified. Successful verification indicates absence of malicious behaviors on the servers, allowing the user to establish trust from the result. To the best of our knowledge, $π$QLB is the first query model designed for blockchain databases with support for confidentiality, integrity, and SQL-like queries.

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arXiv2024

TRACES: TEE-based Runtime Auditing for Commodity Embedded Systems

Control Flow Attestation (CFA) offers a means to detect control flow hijacking attacks on remote devices, enabling verification of their runtime trustworthiness. CFA generates a trace (CFLog) containing the destination of all branching instructions executed. This allows a remote Verifier (Vrf) to inspect the execution control flow on a potentially compromised Prover (Prv) before trusting that a value/action was correctly produced/performed by Prv. However, while CFA can be used to detect runtime compromises, it cannot guarantee the eventual delivery of the execution evidence (CFLog) to Vrf. In turn, a compromised Prv may refuse to send CFLog to Vrf, preventing its analysis to determine the exploit's root cause and appropriate remediation actions. In this work, we propose TRACES: TEE-based Runtime Auditing for Commodity Embedded Systems. TRACES guarantees reliable delivery of periodic runtime reports even when Prv is compromised. This enables secure runtime auditing in addition to best-effort delivery of evidence in CFA. TRACES also supports a guaranteed remediation phase, triggered upon compromise detection to ensure that identified runtime vulnerabilities can be reliably patched. To the best of our knowledge, TRACES is the first system to provide this functionality on commodity devices (i.e., without requiring custom hardware modifications). To that end, TRACES leverages support from the ARM TrustZone-M Trusted Execution Environment (TEE). To assess practicality, we implement and evaluate a fully functional (open-source) prototype of TRACES atop the commodity ARM Cortex-M33 micro-controller unit.

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arXiv2024

A Toolchain for Assisting Migration of Software Executables Towards Post-Quantum Cryptography

Quantum computing poses a significant global threat to today's security mechanisms. As a result, security experts and public sectors have issued guidelines to help organizations migrate their software to post-quantum cryptography (PQC). Despite these efforts, there is a lack of (semi-)automatic tools to support this transition especially when software is used and deployed as binary executables. To address this gap, in this work, we first propose a set of requirements necessary for a tool to detect quantum-vulnerable software executables. Following these requirements, we introduce QED: a toolchain for Quantum-vulnerable Executable Detection. QED uses a three-phase approach to identify quantum-vulnerable dependencies in a given set of executables, from file-level to API-level, and finally, precise identification of a static trace that triggers a quantum-vulnerable API. We evaluate QED on both a synthetic dataset with four cryptography libraries and a real-world dataset with over 200 software executables. The results demonstrate that: (1) QED discerns quantum-vulnerable from quantum-safe executables with 100% accuracy in the synthetic dataset; (2) QED is practical and scalable, completing analyses on average in less than 4 seconds per real-world executable; and (3) QED reduces the manual workload required by analysts to identify quantum-vulnerable executables in the real-world dataset by more than 90%. We hope that QED can become a crucial tool to facilitate the transition to PQC, particularly for small and medium-sized businesses with limited resources.

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arXiv2024

Towards Remotely Verifiable Software Integrity in Resource-Constrained IoT Devices

Lower-end IoT devices typically have strict cost constraints that rule out usual security mechanisms available in general-purpose computers or higher-end devices. To secure low-end devices, various low-cost security architectures have been proposed for remote verification of their software state via integrity proofs. These proofs vary in terms of expressiveness, with simpler ones confirming correct binary presence, while more expressive ones support verification of arbitrary code execution. This article provides a holistic and systematic treatment of this family of architectures. It also compares (qualitatively and quantitatively) the types of software integrity proofs, respective architectural support, and associated costs. Finally, we outline some research directions and emerging challenges.

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arXiv2023

ACFA: Secure Runtime Auditing & Guaranteed Device Healing via Active Control Flow Attestation

Low-end embedded devices are increasingly used in various smart applications and spaces. They are implemented under strict cost and energy budgets, using microcontroller units (MCUs) that lack security features available in general-purpose processors. In this context, Remote Attestation (RA) was proposed as an inexpensive security service to enable a verifier (Vrf) to remotely detect illegal modifications to a software binary installed on a low-end prover MCU (Prv). Since attacks that hijack the software's control flow can evade RA, Control Flow Attestation (CFA) augments RA with information about the exact order in which instructions in the binary are executed, enabling detection of control flow attacks. We observe that current CFA architectures can not guarantee that Vrf ever receives control flow reports in case of attacks. In turn, while they support exploit detection, they provide no means to pinpoint the exploit origin. Furthermore, existing CFA requires either binary instrumentation, incurring significant runtime overhead and code size increase, or relatively expensive hardware support, such as hash engines. In addition, current techniques are neither continuous (only meant to attest self-contained operations) nor active (offer no secure means to remotely remediate detected compromises). To jointly address these challenges, we propose ACFA: a hybrid (hardware/software) architecture for Active CFA. ACFA enables continuous monitoring of all control flow transfers in the MCU and does not require binary instrumentation. It also leverages the recently proposed concept of Active Roots-of-Trust to enable secure auditing of vulnerability sources and guaranteed remediation when a compromise is detected. We provide an open-source reference implementation of ACFA on top of a commodity low-end MCU (TI MSP430) and evaluate it to demonstrate its security and cost-effectiveness.

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arXiv2023

PARseL: Towards a Verified Root-of-Trust over seL4

Widespread adoption and growing popularity of embedded/IoT/CPS devices make them attractive attack targets. On low-to-mid-range devices, security features are typically few or none due to various constraints. Such devices are thus subject to malware-based compromise. One popular defensive measure is Remote Attestation (RA) which allows a trusted entity to determine the current software integrity of an untrusted remote device. For higher-end devices, RA is achievable via secure hardware components. For low-end (bare metal) devices, minimalistic hybrid (hardware/software) RA is effective, which incurs some hardware modifications. That leaves certain mid-range devices (e.g., ARM Cortex-A family) equipped with standard hardware components, e.g., a memory management unit (MMU) and perhaps a secure boot facility. In this space, seL4 (a verified microkernel with guaranteed process isolation) is a promising platform for attaining RA. HYDRA made a first step towards this, albeit without achieving any verifiability or provable guarantees. This paper picks up where HYDRA left off by constructing a PARseL architecture, that separates all user-dependent components from the TCB. This leads to much stronger isolation guarantees, based on seL4 alone, and facilitates formal verification. In PARseL, We use formal verification to obtain several security properties for the isolated RA TCB, including: memory safety, functional correctness, and secret independence. We implement PARseL in F* and specify/prove expected properties using Hoare logic. Next, we automatically translate the F* implementation to C using KaRaMeL, which preserves verified properties of PARseL C implementation (atop seL4). Finally, we instantiate and evaluate PARseL on a commodity platform -- a SabreLite embedded device.

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arXiv2023

On the feasibility of attacking Thai LPR systems with adversarial examples

Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specifically for the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application -- Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attack based on the \emph{semi-targeted} scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate.

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arXiv2022

ASAP: Reconciling Asynchronous Real-Time Operations and Proofs of Execution in Simple Embedded Systems

Embedded devices are increasingly ubiquitous and their importance is hard to overestimate. While they often support safety-critical functions (e.g., in medical devices and sensor-alarm combinations), they are usually implemented under strict cost/energy budgets, using low-end microcontroller units (MCUs) that lack sophisticated security mechanisms. Motivated by this issue, recent work developed architectures capable of generating Proofs of Execution (PoX) for the correct/expected software in potentially compromised low-end MCUs. In practice, this capability can be leveraged to provide "integrity from birth" to sensor data, by binding the sensed results/outputs to an unforgeable cryptographic proof of execution of the expected sensing process. Despite this significant progress, current PoX schemes for low-end MCUs ignore the real-time needs of many applications. In particular, security of current PoX schemes precludes any interrupts during the execution being proved. We argue that lack of asynchronous capabilities (i.e., interrupts within PoX) can obscure PoX usefulness, as several applications require processing real-time and asynchronous events. To bridge this gap, we propose, implement, and evaluate an Architecture for Secure Asynchronous Processing in PoX (ASAP). ASAP is secure under full software compromise, enables asynchronous PoX, and incurs less hardware overhead than prior work.

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