The increasing prevalence of cyber threats necessitates the exploration of cybersecurity challenges in sustainable operating systems. This research paper addresses these challenges by examining the dynamic landscape of cyber threats and the modifications required in operating systems to ensure robust security measures. Through the classification of these threats, the diverse nature of attacks faced by operating systems is revealed, highlighting the need for proactive security measures. Furthermore, the study investigates current cyber security solutions and prevention mechanisms employed to mitigate these threats. It also explores the modifications and challenges that operating systems must undergo in response to cybersecurity crimes, emphasizing the significance of proactive measures to address vulnerabilities exploited by cybercriminals.

Technology has improved, and smart locking systems have become more sophisticated. In this case, the android-based Smart System is primarily intended for multimode operations. Such a system is necessary in banks and businesses since it provides f u n c t i o n s that let users control locks. The implementation’s efficiency the system is incredibly helpful because of its functionality and user-friendly interface. Some homeowners aim to connect their home’s numerous home automation devices. Those connected to a Windows-based PC are the most popular home controllers. In our study, we introduced a form of smart technology that utilized Bluetooth while using a mobile smartphone. Consequently, using it will be simpler and more effective. Additionally, it supported the free and open-source Android and Arduino platforms. This paper proposes a door lock automation system that uses an Android smartphone with Bluetooth as the first piece of hardware. Following a description of the design and software development process, a Bluetooth-based Smartphone application for locking and unlocking doors is demonstrated. The task module acts as the agent in the hardware design for the door-lock system, the Arduino microcontroller serves as the controller and data processing hub, and the solenoid acts as the door lock output. The results of each test show that it is compatible with the original plan for this study.

This paper focuses on the adoption of biometric and RFID security gadgets as innovative solutions for enhancing door lock systems. The traditional reliance on physical keys has proven vulnerable to security breaches, prompting the need for more robust measures. Biometric features such as Fingerprint, Voice and Bluetooth offer unparalleled security by leveraging unique biological characteristics for authentication. Additionally, integrating RFID technology enables convenient access control through assigned cards or tags, eliminating the need for physical keys or complex passwords. The combination of these cutting-edge solutions establishes a comprehensive security infrastructure, significantly reducing risks associated with conventional lock systems. This research highlights the benefits and applications of these technologies in various settings, emphasizing their role in creating a safer environment for individuals and organizations.

Every person must take precautions in the current pandemic crisis, such as wearing protective gear, keeping a safe distance, cleaning their hands, and avoiding touching anything unless absolutely essential. However, there is a potential of disease transmission while touching objects like tables, doors, cars, and other things. Therefore, this study has proposed a proposal to stop them; the project is based on automation, in which an automatic door closing and opening mechanism has been created once the voice command is given. In this scenario, when a proper voice command is provided, the mechanism is activated to open and close the door automatically. Hence that there is no direct human contact with the door, which will assist stop or slow the spread of pandemic disease. The developed Arduino-based module can automatically close and open the door. These devices produced a regulator for the input, which uses the Android s Bluetooth signal. Arduino-based Android customers may easily open and close the door with their phones by communicating via Bluetooth technology. Within range, Bluetooth Classic (BT) makes it possible to connect an Android device. You may open a door with a personalized audio message and operate it by speaking into modules. Here, everyone is using various modules, such as voice commands, to control various modules. When a voice command is provided, the door will open and close on its own.

The advancement of technology is challenging for designers of the security systems. When securing a property or different valuable items, it must be kept into account that often criminals are equipped with performant electric or electronic devices, constructed to disable security systems and to remove any trace of their activity. In consequence, reliable and fast responding security systems must be constructed. This paper proposes a design based on two different microcontrollers, both using Real Time Operating Systems, which has an increased capability to resist at attacks from intruders, and to warn the authorities as soon as a unauthorized access was detected in the secured space. This project is characterized by a low-cost implementation and an efficient operation, given that it is fast responding, and it contains two physically separated modules, making its disabling by intruders more difficult.

In recent years, the advancement of sensor technologies has revolutionized the field of security systems, enabling more accurate and reliable monitoring solutions. Radar technology, known for its ability to operate effectively in various weather conditions and lighting environments, provides a distinct advantage in maintaining consistent surveillance. By utilizing radio waves, the system can accurately detect movement within its coverage area, making it well-suited for both indoor and outdoor applications. The smart radar security system is an innovative approach that harnesses the power of radar technology to create a robust and efficient security solution. This system employs radar sensors to detect and track movement, presence, and object classification in diverse environments, offering significant advantages over traditional security methods. The core functionality of the proposed system revolves around its ability to detect motion with high precision. The proposed approach represents a significant step forward in security solutions by leveraging radar technology s unique advantages. Hence, this work aims to develop a protype for accurately detection the obstacle motion related to radar applications. Bluetooth technology has been utilized for establishing user interface and receiving alerts in the mobile application. Users can receive real-time alerts and notifications on their smartphones or connected devices whenever unauthorized movement is detected. Additionally, the proposed system can be seamlessly integrated with existing smart home or security systems, allowing for comprehensive and centralized surveillance management. As technology continues to evolve, this system showcases the potential for cutting-edge solutions that prioritize both security and privacy.

In today s world, security is a very important issue. People should always keep their belongings safe. To increase security, this research work proposes a IoT-based smart lockers with sensors and access keys with security, verification, and user-friendly tools. This model alerts the user when someone else tries to access their locker and quickly sends an alarm to the authorized user, and provides the option to either grant or reject access to the valid user. In this paper, smart locker is kept registered early to use a locker in the bank, office, home, etc. to ensure safety. The user demands to send an unlock direction with the help of microcontroller NUDE MCU ESP8266 and after accepting the command from the cloud (BLYNK APP), only the user can unlock the closet and access the valuables. This study has also introduced the encroachment detection in lockers with sensors and finally installed smart lockers with fire alarms for security and reliability.

Advances in sensor and communication technologies have transformed traditional homes into smart homes, equipped with sensors and actuators for various functionalities like smart lighting, temperature control, irrigation, solar monitoring, entertainment, and security. This transition is powered by the Internet of Things (IoT) architecture, enabling smart home hubs to integrate and control devices with different communication protocols. However, this shift has also introduced new security and privacy issues in the Smart Home IoT (SH-IoT) environment. To address these challenges, new communication protocols with cryptographic features have been developed, and a unified standard called Matter has been created to promote interoperability among different device manufacturers. This paper presents a comprehensive survey of recent trends and advances in the smart home IoT landscape, focusing on communication protocols, their security issues and protection features against vulnerabilities in the SH-IoT environment.

With the advancement in Internet of things smart homes are rapidly developing. Smart home is the major key component of Internet of thing. With the help of IOT technology we can stay connected to our home appliance. Internet of Things is the Associations of inserted advancements that. Contained physical protests and is utilized to convey and keenness or collaborate with the internal states or the outer surroundings. Rather than individuals to individuals’ correspondence, IoT accentuation on machine-to-machine correspondence. Smart home connects the physical components of our home with the help of software and sensors so that we can access them via internet from one place. Building home automation includes computerizing a home, likewise, mentioned to as a sensible home or smart home. Domestic machines are an urgent part of the Web of Things whenever they are associated with the web. Controlled devices are commonly connected to a focal center or entryway through a domestic automation framework. A smartphone application, tablet PC, personal computer, wall-mounted terminals, or even a web interface that can be gotten to from off-website over the Web are completely utilized by the program to work the framework. Since all the devices are interconnected and interlinked to one an-another they are lot of chances for security breach and data theft. If the security layer is easily breakable any third-party attacker can easily theft the private data of the user. Which leads us to pay more attention to protecting and securing private data. With the day-to-day development of Smart Home, the safety also got to be developed and updated day to day the safety challenges of the IoT for a wise home scenario are encountered, and a comprehensive IoT security management for smart homes has been proposed. This paper acquaints the status of IoT development, and furthermore contains security issues challenges. Finally, this paper surveys the Gamble factor, security issues and challenges in every point of view

In the last decade the rapid development of the communications and IoT systems have risen many challenges regarding the security of the devices that are handled wirelessly. Therefore, in this paper, we intend to test the possibility of spoofing the parameters for connection of the Bluetooth Low Energy (BLE) devices, to make several recommendations for increasing the security of the usage of those devices and to propose basic counter measurements regarding the possibility of hacking them.

Multiple smart operations, similar as smart technologies in homes, smart metropolises, smart husbandry, and smart health and fitness centres, use a new technology known as the Internet of effects. They correspond of an multifariousness of multiple networked bias that link to multiple detectors and the internet. Among the layers that comprise an IoT armature are the perception subcaste, network subcaste, and operation subcaste. Due to their wide use, these smart biases have fairly minimum protection and are vulnerable to attacks. Comprehensive explanations of operation subcaste security issues and protocols, similar as Advance Message Queuing Protocol(AMQP) in application layer protocol, Constrained operation protocol(CoAP), and REST(Emblematic State Transport).

The Internet of Things (IoT) connects the physical world to the digital world, and wireless sensor networks (WSNs) play a significant role. There are billions of IoT products in the market. We found that security was not the primary focus of software developers. The first step of designing a secure product is to analyze and note down the security requirements. This research paper proposes a modified approach, incorporating elements from the SREP (Software Requirements Engineering Process) and SQUARE (Security Quality Requirement Engineering), to define security requirements for IoT products. The revised process is applied to determine the security requirements of a Smart Lock system that utilizes the publish/subscribe protocol MQTT-SN (Message Queuing Telemetry Transport for Sensor Networks) communication protocol architecture.

This paper focuses on the adoption of biometric and RFID security gadgets as innovative solutions for enhancing door lock systems. The traditional reliance on physical keys has proven vulnerable to security breaches, prompting the need for more robust measures. Biometric features such as Fingerprint, Voice and Bluetooth offer unparalleled security by leveraging unique biological characteristics for authentication. Additionally, integrating RFID technology enables convenient access control through assigned cards or tags, eliminating the need for physical keys or complex passwords. The combination of these cutting-edge solutions establishes a comprehensive security infrastructure, significantly reducing risks associated with conventional lock systems. This research highlights the benefits and applications of these technologies in various settings, emphasizing their role in creating a safer environment for individuals and organizations.

The Internet of Things (IoT) is characterized by a myriad of communication protocols that enable seamless connectivity among devices. However, the open nature of the internet exposes these communication protocols to various flaws and vulnerabilities, resulting in the necessity for rigorous security verification. In response to this imperative, the literature abounds with research efforts aimed at assessing the security properties of IoT communication protocols using diverse techniques. In this paper, we present a comprehensive overview of these research endeavors, with a specific focus on the utilization of Formal Methods to verify the security of common communication protocols employed in the IoT.

Today the motorcycle theft cases in Indonesia are in a very high rate and it has been continuing to increase in every year. For this, there is a need for a security system for motorcycle to minimize the risk of motorcycle theft. This study aims to design and create an Internet of Things (IoT)-based security system for the motorcycle that can be controlled through smartphone purposely to reduce the rate of motorcycle thefts. The system was created using Arduino microcontroller. From the results of the implementation, it produced a device that has a number of features including turning on, turning off, and motorcycle starter using application. Another one is the function of tracking to trace the position of the motorcycle. The third feature is to turn off the function of motorcycle socket and the fourth feature is to control the switch engine of motorcycle. The results of the testing in the parameters of maximum distance of Bluetooth connection, delay proses, test of GPS connectivity and test of voice command, showed that it could create a system that can be used well as an anti-theft device for motorcycle.

There will be a billion smart devices with processing, sensing, and actuation capabilities that can be connected to the Internet under the IoT paradigm. The level of convenience, effectiveness, and automation for consumers is expected to rise owing to promising IoT applications. Privacy is a significant concern in IoT systems, and it is essential to provide users with full awareness and control over the data collected by these systems. The use of privacy-enhancing technologies can help to minimise the risks associated with data collection and processing and ensure that user privacy is protected. Lack of standards for devices with limited resources and heterogeneous technologies intensifies the security issue. There are various emerging and existing technologies that can help to address the security risks in the IoT sector and achieve a high degree of trust in IoT applications. By implementing these technologies and countermeasures, it is possible to improve the security and reliability of IoT systems, ensuring that they can be used safely and effectively in a wide range of applications. This article s intent is to provide a comprehensive investigation of the threats and risks in the IoT industry and to examine some potential countermeasures.

Blockchain, as an emerging distributed database, effectively addresses the issue of centralized storage in IoT data, where storage capacity cannot match the explosive growth in devices and data scale, as well as the contradictions arising from centralized data management concerning data privacy and security concerns. To alleviate the problem of excessive pressure on single-point storage and ensure data security, a blockchain data storage method based on erasure codes is proposed. This method involves constructing mathematical functions that describe the data to split the original block data into multiple fragments and add redundant slices. These fragments are then encoded and stored in different locations using a circular hash space with the addition of virtual nodes to ensure load balancing among nodes and reduce situations where a single node stores too many encoded data blocks, effectively enhancing the storage space utilization efficiency of the distributed storage database. The blockchain storage method stores encoded data digest information such as storage location, creation time, and hashes, allowing for the tracing of the origin of encoded data blocks. In case of accidental loss or malicious tampering, this enables effective recovery and ensures the integrity and availability of data in the network. Experimental results indicate that compared to traditional blockchain approaches, this method effectively reduces the storage pressure on nodes and exhibits a certain degree of disaster recovery capability.

An IC used in a safety-critical application such as automotive often requires a long lifetime of more than 10 years. Previously, stress test has been used as a means to establish the accelerated aging model for an IC product under a harsh operating condition. Then, the accelerated aging model is time-stretched to predict an IC’s normal lifetime. However, such a long-stretching prediction may not be very trustworthy. In this work, we present a more refined method to provide higher credibility in the IC lifetime prediction. We streamline in this paper a progressive lifetime prediction method with two phases – the training phase and the inference phase. During the training phase, we collect the aging histories of some training devices under various stress levels. During the inference phase, the extrapolation is performed on the “stressed lifetime” versus the “stress level” space and thereby leading to a more trustworthy prediction of the lifetime.

Physical fitness is the prime priority of people these days as everyone wants to see himself as healthy. There are numbers of wearable devices available that help human to monitor their vital body signs through which one can get an average idea of their health. Advancements in the efficiency of healthcare systems have fueled the research and development of high-performance wearable devices. There is significant potential for portable healthcare systems to lower healthcare costs and provide continuous health monitoring of critical patients from remote locations. The most pressing need in this field is developing a safe, effective, and trustworthy medical device that can be used to reliably monitor vital signs from various human organs or the environment within or outside the body through flexible sensors. Still, the patient should be able to go about their normal day while sporting a wearable or implanted medical device. This article highlights the current scenario of wearable devices and sensors for healthcare applications. Specifically, it focuses on some widely used commercially available wearable devices for continuously gauging patient’s vital parameters and discusses the major factors influencing the surge in the demand for medical devices. Furthermore, this paper addresses the challenges and countermeasures of wearable devices in smart healthcare technology.

A fingerprint architecture based on a micro electro mechanical system (MEMS) for the use as a hardware security component is presented. The MEMS serves as a physically unclonable function (PUF) and is used for fingerprint ID generation, derived from the MEMS-specific parameters. The fingerprint is intended to allow the unique identifiability of electronic components and thus to ensure protection against unauthorized replacement or manipulation. The MEMS chip consists of 16 individual varactors with continuously adjustable capacitance values that are used for bit derivation (“analog” PUF). The focus is on the design-related forcing of random technological spread to provide a wide range of different parameters per chip or wafer to achieve a maximum key length. Key generation and verification is carried out via fingerprint electronics connected to the MEMS, which is realized by an FPGA.

In the realm of Internet of Things (IoT) devices, the trust management system (TMS) has been enhanced through the utilisation of diverse machine learning (ML) classifiers in recent times. The efficacy of training machine learning classifiers with pre-existing datasets for establishing trustworthiness in IoT devices is constrained by the inadequacy of selecting suitable features. The current study employes a subset of the UNSW-NB15 dataset to compute additional features such as throughput, goodput, packet loss. These features may be combined with the best discriminatory features to distinguish between trustworthy and non-trustworthy IoT networks. In addition, the transformed dataset undergoes filter-based and wrapper-based feature selection methods to mitigate the presence of irrelevant and redundant features. The evaluation of classifiers is performed utilising diverse metrics, including accuracy, precision, recall, F1-score, true positive rate (TPR), and false positive rate (FPR). The performance assessment is conducted both with and without the application of feature selection methodologies. Ultimately, a comparative analysis of the machine learning models is performed, and the findings of the analysis demonstrate that our model s efficacy surpasses that of the approaches utilised in the existing literature.

Memristive crossbar-based architecture provides an energy-efficient platform to accelerate neural networks (NNs) thanks to its Processing-in-Memory (PIM) nature. However, the device-to-device variation (DDV), which is typically modeled as Lognormal distribution, deviates the programmed weights from their target values, resulting in significant performance degradation. This paper proposes a new Bayesian Neural Network (BNN) approach to enhance the robustness of weights against DDV. Instead of using the widely-used Gaussian variational posterior in conventional BNNs, our approach adopts a DDV-specific variational posterior distribution, i.e., Lognormal distribution. Accordingly, in the new BNN approach, the prior distribution is modified to keep consistent with the posterior distribution to avoid expensive Monte Carlo simulations. Furthermore, the mean of the prior distribution is dynamically adjusted in accordance with the mean of the Lognormal variational posterior distribution for better convergence and accuracy. Compared with the state-of-the-art approaches, experimental results show that the proposed new BNN approach can significantly boost the inference accuracy with the consideration of DDV on several well-known datasets and modern NN architectures. For example, the inference accuracy can be improved from 18\% to 74\% in the scenario of ResNet-18 on CIFAR-10 even under large variations.

In the landscape of modern computing, fog computing has emerged as a service provisioning mechanism that addresses the dual demands of low latency and service localisation. Fog architecture consists of a network of interconnected nodes that work collectively to execute tasks and process data in a localised area, thereby reducing the delay induced from communication with the cloud. However, a key issue associated with fog service provisioning models is its limited localised processing capability and storage relative to the cloud, thereby presenting inherent issues on its scalability. In this paper, we propose volunteer computing coupled with optimisation methods to address the issue of localised fog scalability. The use of optimisation methods ensures the optimal use of fog infrastructure. To scale the fog network as per the requirements, we leverage the notion of volunteer computing. We propose an intelligent approach for node selection in a trustworthy fog environment to satisfy the performance and bandwidth requirements of the fog network. The problem is formulated as a multi-criteria decision-making (MCDM) problem where nodes are evaluated and ranked based on several factors, including service level agreement (SLA) parameters and reputation value.

IoT scenarios face cybersecurity concerns due to unauthorized devices that can impersonate legitimate ones by using identical software and hardware configurations. This can lead to sensitive information leaks, data poisoning, or privilege escalation. Behavioral fingerprinting and ML/DL techniques have been used in the literature to identify devices based on performance differences caused by manufacturing imperfections. In addition, using Federated Learning to maintain data privacy is also a challenge for IoT scenarios. Federated Learning allows multiple devices to collaboratively train a machine learning model without sharing their data, but it requires addressing issues such as communication latency, heterogeneity of devices, and data security concerns. In this sense, Trustworthy Federated Learning has emerged as a potential solution, which combines privacy-preserving techniques and metrics to ensure data privacy, model integrity, and secure communication between devices. Therefore, this work proposes a trustworthy federated learning framework for individual device identification. It first analyzes the existing metrics for trustworthiness evaluation in FL and organizes them into six pillars (privacy, robustness, fairness, explainability, accountability, and federation) for computing the trustworthiness of FL models. The framework presents a modular setup where one component is in charge of the federated model generation and another one is in charge of trustworthiness evaluation. The framework is validated in a real scenario composed of 45 identical Raspberry Pi devices whose hardware components are monitored to generate individual behavior fingerprints. The solution achieves a 0.9724 average F1-Score in the identification on a centralized setup, while the average F1-Score in the federated setup is 0.8320. Besides, a 0.6 final trustworthiness score is achieved by the model on state-of-the-art metrics, indicating that further privacy and robustness techniques are required to improve this score.

The digitalization and smartization of modern digital systems include the implementation and integration of emerging innovative technologies, such as Artificial Intelligence. By incorporating new technologies, the surface attack of the system also expands, and specialized cybersecurity mechanisms and tools are required to counter the potential new threats. This paper introduces a holistic security risk assessment methodology that aims to assist Artificial Intelligence system stakeholders guarantee the correct design and implementation of technical robustness in Artificial Intelligence systems. The methodology is designed to facilitate the automation of the security risk assessment of Artificial Intelligence components together with the rest of the system components. Supporting the methodology, the solution to the automation of Artificial Intelligence risk assessment is also proposed. Both the methodology and the tool will be validated when assessing and treating risks on Artificial Intelligence-based cybersecurity solutions integrated in modern digital industrial systems that leverage emerging technologies such as cloud continuum including Software-defined networking (SDN).