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This paper presents an efficient and secure method for updating firmware in IoT devices using LoRaWAN network resources and communication protocols. The proposed method involves dividing the firmware into fragments, storing them in the application server's database, and transmitting them to remote IoT devices via downlink messages, without necessitating any changes to the device's class. This approach can be replicated across any IoT LoRaWAN device, offering a robust and scalable solution for large-scale firmware updates while ensuring data security and integrity. The proposed method significantly reduces the downtime of IoT devices and enhances the energy efficiency of the update process. The method was validated by updating a block in the program memory, associated to a specific functionality of the IoT end device. The associated Intel Hex file was segmented into 17 LoRaWAN downlink frames with an average size of 46 bytes. Upon receiving the complete firmware update, the microcontroller employs self-programming techniques that restrict the update process to specific rows of the program memory, avoiding interruptions or reboots. The update process was successfully completed in 51.33 ms, resulting in a downtime of 16.88 ms. This method demonstrates improved energy efficiency compared to existing solutions while preserving the communication network's capacity, making it an adequate solution for remote devices in LoRaWAN networks. [ABSTRACT FROM AUTHOR]

The increasing connectivity and complexity of automotive systems require enhanced mechanisms for firmware updates to ensure security and integrity. Traditional methods are insufficient for modern vehicles that require seamless over-the-air (OTA) updates. Current OTA mechanisms often lack robust security measures, leaving vehicles vulnerable to attacks. This paper proposes an innovative approach based on the use of decentralized identifiers (DIDs) and distributed ledger technology (DLT) for secure OTA firmware updates of on-vehicle software. By utilizing DIDs for unique vehicle identification, as well as verifiable credentials (VCs) and verifiable presentations (VPs) for secure information exchange and verification, the solution ensures the integrity and authenticity of software updates. It also allows for the revocation of specific updates, if necessary, thereby improving overall security. The security analysis applied the STRIDE methodology, which enabled the identification of potential threats, including spoofing, tampering, and privilege escalation. The results showed that our solution effectively mitigates these threats, while a performance evaluation indicated low latency during operations. [ABSTRACT FROM AUTHOR]

Wireless networking provides many advantages, but it also coupled with new security threats and alters the organization's overall information security risk profile. Meanwhile, researchers are also actively working to enhance the security of embedded devices. However, previous studies have overlooked the insecurity of a particular device category, known as End-of-Life (EoL) devices. When a product reaches its EoL phase, vendors discontinue its maintenance, including the provision of bug fixes and security patches. However, many EoL devices remain on the internet with several critical vulnerabilities, thereby creating a fertile ground for botnets and cyber-attacks. Due to the lack of security updates, hardening the potentially vulnerable firmware in IoT devices is the most direct and promising defense method, but it has not been fully explored. In this paper, we propose a systematic active defense approach to harden EoL IoT devices, utilizing a hybrid binary rewriting method to monitor high-risk APIs and filter attack vectors. The proposed system, called Harden-IoT, consists of three tightly coupled modules: suspicious code snippets location, attack vector interception, and heuristic firmware repackaging. It can reinforce different architecture (MIPS, ARM) Linux-Based IoT devices without source code. We evaluate the effectiveness, adaptability, and overhead of the method using 23 firmware images sourced from various vendors. The results show that Harden-IoT can effectively defend against multiple types of vulnerabilities under low overhead conditions while also being able to adapt to different heterogeneous devices. [ABSTRACT FROM AUTHOR]

To protect important data and files, people often use air gap isolation, also known as air gap separation, to block external threats. However, internal networks may still introduce pollution due to supply chain contamination, human error, or social engineering. Although internal devices cannot directly communicate with the outside world. This paper proposes a new technology called Bit Sufi-Dance that utilizes electricity meters and optical devices to detect exfiltrated data. Most electricity meters have power indicator mechanical turntables or LED lights which can be indirectly controlled by the device's power consumption oscillation. This allows for information encoding and the extraction of data from the air-gapped computer. It is important to note that this exfiltration channel does not require any hardware or firmware modifications and cannot be detected by existing Data Leakage Prevention (DLP) systems. The article discusses its design and implementation issues while evaluating it using different types of electricity meters. Our experiment demonstrates that data can be exfiltrated from the air-gap isolated computer through an electricity meter at a bit rate of 101 b/h. Finally, we assess this security threat and discuss defense mechanisms and preventive measures. [ABSTRACT FROM AUTHOR]

With the development of Internet of things technology, intelligent terminals of the Internet of things have gained popularity. At present, there are many security vulnerabilities in the firmware of the Internet of things terminal, and it is very inconvenient to use manual methods to detect the vulnerabilities of the Internet of things terminal equipment. The intelligent fuzzing technology based on genetic algorithms is mainly used, and the firmware to be tested is automatically tested using random variation data. Aiming at the low efficiency of the existing fuzzing technology based on genetic algorithms, this paper proposes an intelligent fuzzing model based on multiple reinforcement learning algorithms. In this model, reinforcement learning algorithms are used to optimize the mutation operator selection strategy of fuzzing and the code coverage of fuzzing is improved by intelligently selecting different mutation operators for different test cases. This paper compares the performance of DDQN, DDPG, TRPO, and PPO algorithms in the model through comparative experiments on LAVA datasets and traditional fuzzing methods. The results show that in the fuzzing environment, there are significant differences in the performance of different algorithms for different target programs and the fuzzing method based on reinforcement learning is obviously superior to the traditional fuzzing method, proving the proposed model' s availability and effectiveness. [ABSTRACT FROM AUTHOR]

The number of Internet-of-Things (IoT) devices has been increasing rapidly every year. Most of these devices have access to important personal data such as health, daily activities, location, and finance. However, these devices have security problems since they have limited processing power and memory to implement complex security measures. Therefore, they possess weak authentication mechanisms and a lack of encryption. Additionally, there are no widely accepted standards for IoT security. Device Identifier Composition Engine (DICE) was proposed as a standard that enables adding a security layer to low-cost microcontrollers with minimal silicon overhead. However, previous studies show that DICE-based attestation is vulnerable to some remote attacks. In this study, we present a novel method called DICEguard to address the security problems of DICE. One of the key innovations of DICEguard is its incorporation of periodic memory forensics (PMF) technique, leveraging a hardware-based hash engine to detect and mitigate potential security breaches resulting from firmware vulnerabilities. DICEguard enhances the overall resilience of IoT devices against attacks by swiftly detecting alterations indicative of malicious activity through periodic calculation and comparison of firmware digests. Furthermore, DICEguard introduces a one-time programmable (OTP) memory component to safeguard critical security parameters, such as public keys used for signature verification, against tampering by adversaries. This ensures the integrity of essential security measures even in the face of sophisticated attacks. We implemented the enhanced DICE architecture using the open-source RISC-V platform Ibex and the mbedTLS library for cryptographic operations. We performed the hash operations required by DICE in a hardware-based manner on a commercial Field Programmable Gate Array (FPGA) platform rather than firmware, which is more vulnerable to attacks. Our test results show that with negligible area overhead to a standard microcontroller system, the proposed method can detect the simulated attacks. [ABSTRACT FROM AUTHOR]

Screws are a versatile method of fixation and are often used in orthopaedic surgery. Various specialised geometries are often used for bone screws to optimise their fixation strengths in limited spaces at the expense of manufacturing costs. Additionally, ongoing research is looking to develop systems/models to automatically optimise bone screw tightening torques. For both applications, it is desirable to have a test rig for inserting screws in a regulated, instrumented, and repeatable manner. This work presents such a test rig primarily used for the validation of optimal torque models; however, other applications like the above are easily foreseeable. Key features include controllable insertion velocity profiles, and a high rate measurement of screw torque, angular displacement, and linear displacement. The test rig is constructed from mostly inexpensive components, with the primary costs being the rotational torque sensor (approx. 2000 €), and the remainder being approximately 1000 €. This is in comparison to a biaxial universal testing machine which may exceed 100,000 €. Additionally, the firmware and interface software are designed to be easily extendable. The angular velocity profiling and linear measurement repeatability of the test rig is tested and the torque readings are compared to an off-the-shelf static torque sensor. [ABSTRACT FROM AUTHOR]

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm. [ABSTRACT FROM AUTHOR]

Projections for future air mobility envisage intensely utilized airspace that does not simply scale up from existing systems with centralized air traffic control. This paper considers the implementation and test of a software and hardware framework for decentralized control of aerial vehicles within intensely used airspace. Up to 10 rotary wing vehicles of maximum all up mass of 1 kg are flown in an outdoor volume with length scale of 100 m with GPS and WiFi connectivity. Flight control is implemented using a Pixhawk 4 flight controller running the PX4 firmware with guidance algorithms run on a separate onboard companion computer. Deconfliction is implemented using a simple elastic repulsion model with a guidance update rate of 10 Hz . Traffic structures are constructed from a path of directed waypoints and associated cross sectional geometry. Junctions are implemented when two paths converge into one or when one path diverges into two. Agents engage with structures through execution of flow, merge and swirl velocity rules. Calibration experiments showed that the worst case latency in agents sharing position information was of the order of 0.5 s made up from delays due to finite guidance update rate, WiFi processing and centralized message processing. A choice of vehicle cruise speed of 2 m∕s and conflict radius of 2.5 m provided an acceptable compromise between experiment time efficiency (speed) and spatial efficiency (resolution) within the test volume. Results from recirculating junction experiments show that peak deconfliction activity occurs at the junction node, however biased distribution of agents within a corridor means the peak intensity is pushed ahead of the node. Use of meshed helical junction structures significantly reduces the intensity of conflict at the expense of reduced junction time efficiency. [ABSTRACT FROM AUTHOR]

The rapid evolution of network communication technologies has led to the emergence of new forms of malware and cybercrimes, posing significant threats to user safety, network infrastructure integrity, and data privacy. Despite efforts to develop advanced algorithms for detecting malicious activity, constructing models that are both accurate and reliable remains a challenge, especially in handling vast and dynamically shifting data patterns. The prevalent bag-of-words (BOW) method, while widely used, falls short in capturing crucial spatial and sequence information vital for detecting malware patterns. To address this challenge, the work presented in this paper proposes hybrid convolution neural network-long short-term memory network (CNN-LSTM) combination models, leveraging CNN's spatial information extraction and LSTM's temporal modeling capabilities. Focused on predicting the infiltration of malicious software into personal computers, the proposed hybrid CNN-LSTM model considers factors such as location, firmware version, operating system, and anti-virus software. The proposed models undergo training and evaluation using Microsoft's malware dataset, demonstrating superior performance compared to traditional CNN and LSTM models. The CNN-LSTM model achieves an impressive accuracy of 95% on the Microsoft malware dataset, highlighting its effectiveness in malware detection. [ABSTRACT FROM AUTHOR]

Industrial robotics has advanced significantly as a result of automation's quick progress, and its applications in biomedicine, particularly in rehabilitation technology are growing. The goal of this project is to design, construct, manufacture and test a low-cost, six-degree-of-freedom 3D-printed Thor robotic arm that is controlled by surface electromyography (sEMG) signals. These signals were gathered from nine healthy volunteers utilizing a wireless Myo motions armband, and they made it possible to distinguish between seven different hand gestures. Because of its mechanical robustness, polylactic acid (PLA) was employed in 3D printing, which added to the Thor prototype's endurance and open-source nature. Two Arduino Mega controllers and Ramps interface boards improved the electronic system, and Marlin firmware and Arduino-based software were used to govern control. Workspace navigation was used to evaluate the system and install an extra stepper motor to increase torque at the base joint. Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), and Linear Discriminant Analysis (LDA) were the three classifiers used for gesture detection. With an accuracy of 95.04% for dominant hands and 83.41% for non-dominant hands, SVM demonstrated the best accuracy. Additionally, SVM achieved near-perfect accuracy in elbow flexion-extension identification (99.55% for dominant and 98.73% for non-dominant), while LDA and K-NN also shown strong performance. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices have become increasingly important within the smart home domain, making the security of the devices a critical aspect. The majority of IoT devices are black-box systems running closed and pre-installed firmware. This raises concerns about the trustworthiness of these devices, especially considering that some of them are shipped with a microphone or a camera. Remote attestation aims at validating the trustworthiness of these devices by verifying the integrity of the software. However, users cannot validate whether the attestation has actually taken place and has not been manipulated by an attacker, raising the need for HCI research on trust and understandability. We conducted a qualitative study with 35 participants, investigating trust in the attestation process and whether this trust can be improved by additional explanations in the application. We developed an application that allows users to attest a smart speaker using their smartphone over an audio channel to identify the attested device and observe the attestation process. In order to observe the differences between the applications with and without explanations, we performed A/B testing. We discovered that trust increases when additional explanations of the technical process are provided, improving the understanding of the attestation process. [ABSTRACT FROM AUTHOR]

Through providing an emulated environment modeled from embedded devices, firmware rehosting enables dynamic analysis on embedded device firmware. Existing fullemulation firmware rehosting solutions can only preventatively fix known hardware and software dependencies but cannot address previously-unknown dependencies during the rehosting process. In this paper we propose FirmDep, an embedded application rehosting solution assisted with dynamic analysis. During the rehosting process, FirmDep records the execution trace and system state of the embedded application to be analyzed. If FirmDep fails to rehost the application, FirmDep extracts information and recovers system states from the execution trace, and then uses several strategies to identify and arbitrate the unresolved dependency problems. We implemented the prototype system of FirmDep based on PANDA and angr, and tested it with embedded Web applications from 217 real-world firmware images. The results show that FirmDep can effectively identify unresolved dependencies of embedded applications and improve the success rate of rehosting. [ABSTRACT FROM AUTHOR]

The preface to the special issue on System and Cyberspace Security in the International Journal of Software & Informatics discusses the importance of addressing security issues in emerging system software and web applications due to the increasing frequency of system attacks and cybercrime. The document highlights research achievements in binary code security, source code vulnerability detection, privacy compliance and protection, and emerging underground software and applications. It also provides summaries of five representative articles focusing on various aspects of system and cyberspace security, authored by experts in the field from different universities in China. [Extracted from the article]

Embedded systems play a critical role in modern technology, yet traditional firmware development presents significant challenges, including steep learning curves, debugging complexities, and limited accessibility. To address these issues, we introduce Code-Gen Assist, an innovative software solution leveraging AI-powered natural language programming. Code-Gen Assist enables users to describe desired program functionality in natural language, democratizing embedded systems development and reducing the barrier to entry for newcomers. By automatically generating and executing code based on user inputs, it streamlines the development process, facilitating rapid prototyping and iteration. Embedded systems are ubiquitous in modern technology, yet their complexity often hinders accessibility for aspiring learners. This project tackles this challenge by proposing a paradigm shift in the learning approach for embedded systems. We aim to democratize access to this field by simplifying electronics and microcontroller programming, fostering engagement and enthusiasm. To bridge the gap between complexity and usability, our project utilizes cutting- edge components like the Raspberry Pi 4 Model B, a high-quality display and mini keyboard. This integration creates a tangible and versatile environment for users to explore embedded systems. Our innovative approach aims to not only enhance the learning experience but also lower the barrier to entry, igniting passion and fostering a new generation of embedded systems enthusiasts. This paper presents the design and implementation of Code-Gen Assist, highlighting its potential to revolutionize embedded systems development. Through evaluation of its accuracy, reduction in development time, and user experience, we demonstrate the feasibility and benefits of AI-powered natural language programming in this domain. [ABSTRACT FROM AUTHOR]

Over the last years, a huge number of things has been connected to the Internet to support applications for smart cities, smart industries, smart agriculture and smart environment, etc. In order to fix bugs, update features or perform dynamic adaptations on these things, Efficient Firmware Update Over the Air (FUOTA) methods are required. In this paper, we propose a modular-based approach for the development of firmware of MCU-based IoT devices, and a new FUOTA method for devices communicating using LoRaWAN. This method enables partial and dynamic updates of modules forming the device firmware, and does not require a system reboot to apply these updates. This approach and this method have been implemented on STM32 MCUs. The experimental results show good performance in terms of update size and network traffic compared to the traditional FUOTA method and monolithic-based firmware development approach, which both impose full firmware updates. [ABSTRACT FROM AUTHOR]

Due to its plug-and-play functionality and wide device support, the universal serial bus (USB) protocol has become one of the most widely used protocols. However, this widespread adoption has introduced a significant security concern: the implicit trust provided to USB devices, which has created a vast array of attack vectors. Malicious USB devices exploit this trust by disguising themselves as benign peripherals and covertly implanting malicious commands into connected host devices. Existing research employs supervised learning models to identify such malicious devices, but our study reveals a weakness in these models when faced with sophisticated data poisoning attacks. We propose, design and implement a sophisticated adversarial data poisoning attack to demonstrate how these models can be manipulated to misclassify an attack device as a benign device. Our method entails generating keystroke data using a microprogrammable keystroke attack device. We develop adversarial attacker by meticulously analyzing the data distribution of data features generated via USB keyboards from benign users. The initial training data is modified by exploiting firmware-level modifications within the attack device. Upon evaluating the models, our findings reveal a significant decrease from 99 to 53% in detection accuracy when an adversarial attacker is employed. This work highlights the critical need to reevaluate the dependability of machine learning-based USB threat detection mechanisms in the face of increasingly sophisticated attack methods. The vulnerabilities demonstrated highlight the importance of developing more robust and resilient detection strategies to protect against the evolution of malicious USB devices. [ABSTRACT FROM AUTHOR]

With the increase of IoT devices generating large amounts of user-sensitive data, improper firmware harms users' security and privacy. Latest home appliances are integrated with features to assure compatibility with smart home IoT. However, applying complex security mechanisms to IoT is limited by device hardware capabilities, making them vulnerable to attacks. Such attacks have recently become frequent. To address this issue, we developed a secure verification mechanism for firmware released by the device's manufacturer. We proposed an IoT gateway for secure firmware verification and updating for smart home IoT devices utilizing the IOTA MAM (Masked Authenticated Messaging) protocol and a distributed file system with IPFS (Inter-Planetary File System) protocol. These two communication protocols ensure decentralized communication and firmware file distribution between the IoT device vendor and the IoT end device. The proposed scheme securely shares latest firmware content over IOTA and IPFS networks, performs a secure firmware update on IoT end devices and ensures authenticity and integrity of the firmware. Two types of validation methods were proposed for firmware updating and validation. We implemented the proposed scheme using three entities, Vendor, IoT gateway, and IoT end device. Our system yielded promising results in performing secure automated firmware updates on IoT end devices with very low computational power. The system's functionality was implemented using IOTA's MAM run on Raspberry Pi as an IoT gateway along with an ESP8266 Wi-Fi microcontroller, demonstrating the effectiveness of our approach. Our proposed methodology can be used for secure firmware distribution on home IoT applications. [ABSTRACT FROM AUTHOR]

The firmware developed for the readout and trigger processing of the information emerging from the BIS78-RPC Muon Spectrometer chambers in the ATLAS experiment at CERN is presented here, together with data processing techniques, data acquisition software, and tests of the readout chain system, which represent efforts to make these chambers operational in the ATLAS experiment. This work is performed in the context of the BIS78-RPC project, which deals with the pilot deployment of a new generation of sMDT+RPCs in the experiment. Such chambers are planned to be fully deployed in the whole barrel inner layer of the Muon Spectrometer during the Phase II upgrade of the ATLAS experiment. On-chamber front-ends include an amplifier, a discriminator ASIC, and an LVDS transmitter. The signal is digitized by CERN HPTDC chips and then processed by an FPGA, which is the heart of the readout and trigger processing, using various techniques. [ABSTRACT FROM AUTHOR]

In the evolving landscape of cybersecurity threats, permanent denial-of-service (PDoS) attacks have emerged as a particularly damaging form of cyber aggression. Unlike the more well-known denial-of-service (DoS) attacks, which disrupt services temporarily, PDoS attacks aim to inflict irreversible damage to systems, often resulting in significant system overhauls and requiring hardware replacement. To enable the development of effective security measures, but also to address the knowledge gaps, this paper presents an in-depth exploration of PDoS attacks, emphasizing their distinguishing characteristics, underlying mechanisms, and potential further development. Through a comprehensive case study, this research highlights diverse tactics and strategies employed by attackers, from targeting IoT devices to manipulating boot processes and exploiting firmware vulnerabilities. A novel classification of PDoS attack vectors is proposed that also explains the ways in which the systems can be compromised. The findings confirm the pressing need for adaptive and robust defense mechanisms to mitigate the threats posed by PDoS attacks in our interconnected digital world. [ABSTRACT FROM AUTHOR]

Smart home devices are becoming more popular over the years. A diverse range of appliances is being created, and Ambient Intelligence is growing in homes. However, there are various producers of these gadgets, different kinds of protocols, and diverse environments. The lack of interoperability reduces comfort of the user and turns into a barrier to smart home adoption. Matter is growing by constructing an open-source application layer protocol that can be compatible with all smart home ecosystems. In this article, a Matter overview is provided (namely, of the Commissioning stage), and a Matter Accessory using ESP32-S3 is developed referring to the manufacturer's SDKs and is inserted into an existent household ecosystem. Its behavior on the network is briefly analyzed, and interactions with the device are carried out. The simplicity of these tasks demonstrates accessibility for developers to create products, especially when it comes to firmware. Additionally, device commissioning and control are straightforward for the consumer. This capacity of gadget incorporation into diverse ecosystems using Matter is already on the market and might result in higher device production and enhanced smart home adoption. [ABSTRACT FROM AUTHOR]

In this brief, a real-time inversion of petrophysical information in downhole firmware for nuclear magnetic resonance logging while drilling (NMR-LWD) based on a field-programmable gate array (FPGA) is proposed. Firstly, the inverse problem is introduced, the nonnegative least-squares (NNLS) problem is modeled and solved by the accelerated projected gradient descent (APGD) algorithm. Secondly, the efficient architecture for the APGD accelerator based on look-up tables (LUTs) is used in Intel's 10M50 FPGA. Finally, a software cooling method based on a multi-core structure and periodic thermal management technology is deployed. The proposed scheme is applied to NMR-LWD, the effectiveness and feasibility of the proposed scheme are verified experimentally. [ABSTRACT FROM AUTHOR]

The widespread deployment of unattended LoRa networks poses a growing need to perform Firmware Updates Over-The-Air (FUOTA). However, the FUOTA specifications dedicated by LoRa Alliance fall short of several deficiencies with respect to energy efficiency, transmission reliability, multicast fairness, and security. This article proposes FLoRa+, energy-efficient, reliable, beamforming-assisted, and secure FUOTA for LoRa networks, which is featured with several techniques, including delta scripting, channel coding, beamforming, and securing mechanisms. Specifically, we first propose a joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Then, we design a concatenated channel coding scheme with outer rateless code and inner error detection to enable reliable transmission for coding gain. Afterward, we develop a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Finally, we present a securing mechanism incorporating progressive hash chain and packet arrival time pattern verification to countermeasure firmware integrity and availability attacks for security gain. Experimental results on a 20-node testbed demonstrate that FLoRa+ improves transmission reliability and energy efficiency by up to 1.51× and 2.65× compared with LoRaWAN. Additionally, FLoRa+ can defend against 100% and 85.4% of spoofing and Denial-of-Service (DoS) attacks. [ABSTRACT FROM AUTHOR]

This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portability, and non-radiation. The system is capable of reconstructing electrical conductivity tomographic images at a rate of 30.624 frames per second, taking about 5 min for imaging. The current source employs a 0.5 mA adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. This article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians to obtain images of respiratory diseases. The modified reconstruction method GREIT (EIDORS) was evaluated with experimental data and was compared with the Gauss–Newton and Total Variation methods, where GREIT 2D proved to be superior. [ABSTRACT FROM AUTHOR]

The main goal of the article is to present the concept of using a simulation environment when designing an advanced fibre-optic seismometer (FOS) using a field-programmable gate array (FPGA) computing system. The first part of the article presents the advanced requirements regarding the FOS principle of operation, as well as the measurement method using a closedloop operation. The closed-loop control algorithm is developed using the high-level language C++ and then it is synthesised into an FPGA. The following part of the article describes the simulation environment developed to test the operation of the control algorithm. The environment includes a model of components of the measurement system, delays, and distortions in the signal processing path, and some of the measurement system surroundings. The article ends with a comparison of simulation data with measurements. The obtained results are consistent and prove correctness of the methodology adopted by the authors. [ABSTRACT FROM AUTHOR]

This article presents a new firmware update paradigm for optimising the procedure in microcontrollers. The aim is to allow updating during program execution, without interruptions or restarts, replacing only specific code segments. The proposed method uses static and absolute addresses to locate and isolate the code segment to be updated. The work focuses on Microchip's PIC18F27K42 microcontroller and includes an example of updating functionality without affecting ongoing applications. This approach is ideal for band limited channels, reducing the amount of data transmitted during the update process. It also allows incremental changes to the program code, preserving network capacity, and reduces the costs associated with data transfer, especially in firmware update scenarios using cellular networks. This ability to update the normal operation of the device, avoiding service interruption and minimising downtime, is of remarkable value. [ABSTRACT FROM AUTHOR]

Vibration analysis is commonly used to assess machinery conditions, earthquake detection, and structural monitoring. Commercially available DAQs (Data Acquisition Systems) feature high costs and limited versatility in terms of end-user hardware/firmware customization, making it difficult to adapt them to the input signal features and add supplementary functionalities. Hence, this research aims to develop a custom acquisition board for detecting vibration signals via IEPE (Integrated Electronic Piezoelectric) sensors, considering the limitations of commercially available systems, and building upon solutions found in the literature. The DAQ board was intended for remote vibration monitoring of infrastructure and machinery for industrial applications, allowing the implementation of predictive maintenance strategies. The proposed DAQ board has two independent and fully configurable channels, which can be set for acquiring signals from IEPE sensors or generic voltage sources. The DAQ board relies on the STM32F401 microcontroller to manage the acquisition from high-speed ADCs, process data, and store them in mass memory (SD card). During acquisition, the DAQ implements a batch acquisition strategy based on a buffer flash memory for temporarily storing ADCs data, which are iteratively poured into mass memory. Also, the board has Bluetooth connectivity to transmit acquired data and receive commands remotely. A prototype of the DAQ board was developed and tested with several waveforms, including vibration signals. The tests showed that the board can acquire vibration signals and compute the FFT onboard. The DAQ demonstrated a good balance between performance, accuracy, flexibility, and cost, making it suitable for several industrial applications and allowing for scalability and integration potential. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) era arrives, the proliferation of IoT devices exposed to the Internet presents a significant challenge to device security. Firmware is software that operates within Internet of Things (IoT) devices, directly governing their behaviors and functionalities. Consequently, the security of firmware is critical to shielding IoT devices from potential threats. In order to enable users to operate a device intuitively, firmware commonly provides a web interface. Consequently, this interface frequently serves as the primary attack goal in Internet of Things (IoT) devices, rendering them susceptible to numerous cyber-attacks. Unfortunately, web services have complex data interactions and implicit dependencies, and it is not easy to balance efficiency and accuracy during the analysis process, leading to heavy overhead. This paper proposes a lightweight vulnerability scanning approach, WFinder, designed explicitly for embedded firmware web services to perform vulnerability checks on backend binary files in firmware. WFinder uses static analysis to focus on identifying vulnerabilities in boundary binary files related to web services in firmware. Initially, the approach identifies boundary binary files and external data entry points based on front-end and back-end associativity features. Subsequently, rules are formulated to filter hazardous functions to narrow the analysis targets. Finally, the method generates sensitive call paths from the external data input points to the hazardous functions and conducts a lightweight taint analysis along these paths to uncover potential vulnerabilities. We implemented a prototype of WFinder and evaluated it on the firmware of ten devices from five well-known manufacturers. We discovered thirteen potential vulnerabilities, eight of which were confirmed by the CNVD, and assigned them CNVD identification numbers. Compared with the most advanced tool, SATC, WFinder was more efficient at discovering more bugs on the test set. These results indicate that WFinder is effective at detecting bugs in embedded web services. [ABSTRACT FROM AUTHOR]

The escalating advancement in Software-Defined Vehicles (SDVs) necessitates a formidable strategy for firmware updates, where traditional methods often fall short of guaranteeing absolute integrity. Although decentralization has been explored in studies for firmware integrity verification using blockchain technology, it lacks comprehensive validation in the context of automotive over-the-air (OTA) updates. By recognizing the limitations of current practices and the partial validation of decentralized approaches, such as blockchain, in the automotive sector, our study introduces a novel mechanism for firmware over-the-air (FOTA) updates. This mechanism is grounded in the widely adopted message queuing telemetry transport (MQTT) protocol, integral to the Internet of Things (IoT) domain, and leverages Merkle tree-based blockchain verification to fortify the fidelity and efficiency of firmware updates. Our proposed solution not only prioritizes the stability crucial to automotive OTA updates but also ensures that performance is not compromised. This dual focus on reliability and efficiency represents a significant stride forward in the development of secure, scalable SDV firmware update protocols. [ABSTRACT FROM AUTHOR]

Reverse engineering is the process of understanding the inner working of a software system without having the source code. It is critical for firmware security validation, software vulnerability research, and malware analysis. However, it often requires a significant amount of manual effort. Recently, data-driven solutions were proposed to reduce manual effort by identifying the code clones on the assembly or the source level. However, security analysts still have to understand the matched assembly or source code to develop an understanding of the functionality, and it is assumed that such a matched candidate always exists. This research bridges the gap by introducing the problem of assembly code summarization. Given the assembly code as input, we propose a machine-learning-based system that can produce human-readable summarizations of the functionalities in the context of code vulnerability analysis. We generate the first assembly code to function summary dataset and propose to leverage the encoder-decoder architecture. With the attention mechanism, it is possible to understand what aspects of the assembly code had the largest impact on generating the summary. Our experiment shows that the proposed solution achieves high accuracy and the Bilingual Evaluation Understudy (BLEU) score. Finally, we have performed case studies on real-life CVE vulnerability cases to better understand the proposed method's performance and practical implications. [ABSTRACT FROM AUTHOR]