Security Devices Research Articles

The Critical Role of Two-Factor Authentication (2FA) in Mitigating Ransomware and Securing Backup, Recovery, and Storage Systems

In today’s digital landscape, ransomware attacks have become one of the most significant cybersecurity threats. Securing backup, recovery, and storage systems is paramount to ensuring business continuity. Two-Factor Authentication (2FA) has proven to be a key strategy in defending against ransomware attacks by adding an extra layer of security. By requiring two forms of authentication, 2FA reduces the risk of unauthorized access to critical systems and data. This paper explores how 2FA enhances the security of backup systems, recovery processes, and storage devices, with a particular focus on its role in mitigating ransomware risks. It further discusses its impact on compliance with data protection regulations and operational efficiency.

Countermeasures against bias and spoofing in modern facial recognition systems

Facial recognition systems (FRS) have become integral to modern security, authentication, and surveillance applications, driven by advancements in computer vision and deep learning. These systems promise unparalleled accuracy and efficiency, revolutionizing industries ranging from law enforcement to personal device security. However, their widespread adoption has exposed critical vulnerabilities, particularly bias and spoofing attacks. Bias in facial recognition stems from imbalanced training datasets, leading to disparities in recognition accuracy across gender, ethnicity, and age groups. These biases raise ethical concerns, compromise system reliability, and undermine trust in automated decision-making processes. Spoofing attacks, involving techniques such as mask-based or image-based impersonation, exploit system weaknesses to bypass security measures, posing significant risks to sensitive applications like financial transactions and border control. This research explores countermeasures to address these challenges, presenting a dual approach combining data-centric and algorithmic strategies. To mitigate bias, techniques such as dataset augmentation, adversarial debiasing, and fairness-aware learning are examined, ensuring equitable performance across diverse user groups. Anti-spoofing measures, including liveness detection, multispectral imaging, and adversarial training, are discussed to enhance system robustness against impersonation attempts. Additionally, the study highlights the role of explainable artificial intelligence (XAI) in fostering transparency and accountability in FRS. By integrating these countermeasures into system design, developers can build facial recognition solutions that are both secure and inclusive, balancing performance with ethical considerations. This research provides a comprehensive framework for enhancing the reliability and trustworthiness of modern facial recognition technologies.

Design of Automatic Security System Based Internet of Things at the Museum

This research aims to determine the procedures for developing an Internet of Things (IoT)-based Automatic Security System as a security device for museum objects and to test its feasibility based on user feedback in museums. The Design Thinking model used consists of five stages: Empathize (understanding the user’s perspective), Define (gathering information on the encountered problems), Ideate (generating innovative ideas), Prototype (creating a prototype of the obtained ideas, ensuring the functionality of each device feature, and validating the product through expert judgment for feedback and product revision), and Testing (evaluating the system’s feasibility using Usability standards based on ISO/IEC 9241-11, focusing on Effectiveness, Efficiency, and Satisfaction with user involvement). The research results indicate that the IoT-based Automatic Security System has met optimal outcomes based on Usability aspects, namely: the Effectiveness analysis showed a ratio above 100% with a Very Effective level of achievement; the Efficiency aspect analysis received user feedback with a device usage rate of 1.35 goals per second; and the Satisfaction analysis, using the System Usability Scale (SUS) questionnaire, scored 78.25, indicating an Excellent level, suggesting that the system is worthy of use.

A Decentralized Storage and Security Engine (DeSSE) Using Information Fusion Based on Stochastic Processes and Quantum Mechanics

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data.

Innovative resource-saving security strategies for IoT devices

The astounding trend of increasing the number of connected IoT devices reflects the growing importance of this technology in industry, healthcare, the domestic sphere, and other sectors. However, with the expansion of IoT capabilities, the number of challenges also rises, particularly regarding the security of these devices, many of which are characterised by limited resources such as memory, power consumption, computational power, and network bandwidth. This article examines the key challenges associated with ensuring the security of IoT devices and proposes potential solutions and optimisation strategies that consider these limitations. The primary focus is developing and analysing lightweight cryptographic algorithms capable of providing robust data protection with minimal resource usage. The article also discusses efficient energy management strategies and optimising memory usage in IoT devices. Emphasis is placed on developing adaptive security mechanisms that can effectively respond to dynamic operational conditions and resource constraints. It is noted that further research and development should focus on creating integrated solutions that combine hardware, software, and managerial aspects to optimise the overall efficiency and security of IoT systems.

An Anthracene-Imidazoanthraquinone Conjugate Exhibiting Ratiometric Fluorescence Turn - on Behavior with CN- and F- Anions.

A new conjugate, 2-(4-(anthracen-9-yl) phenyl)-[1,2-d]imidazole-1H-anthraquninone (AQI) has been designed and synthesized as a molecular probe 4. The photophysical and electrochemical behavior of the probe in the absence and presence of different class of ions were examined in acetonitrile solution. The probe 4 with F- and CN- anions showed ratiometric fluorescence "turn - On" response due to variation in ICT processes. Cyclic voltammetry of probe exhibited reversible redox behavior wherein the band gap (Eg =1240/λmax) of probe (ΔE =3.220 eV) decreased (~2.583 eV) after the interaction with F- and CN- anions. The probe interacted with both anions in a 1 : 1 stoichiometry with good binding constants (KF -=.05×106 M-1 and KCN - = 1.46×106 M-1) and limit of detection/quantification (LOD/LOQ) in nM range. pH studies showed that probe 4 has potential to detect the anions under physiological conditions (between pH 6-10). The probe upon interaction with both F- and CN- anions showed a naked-eye sensitive color change in solution and on test paper strips. The probable complexes, 4+F-/CN- upon interaction with trifluoroacetic (TFA) acid showed reversible behavior wherein the intensity of probe rejuvenated. The output emission signal of the probe upon providing F- and TFA as a chemical inputs mimic the function of a memory device with ''write-read-erase-read'' functions and has also been utilized to construct a molecular key-pad lock security device system. Also, the probe showed sensitivity to detect the F- in toothpaste. The mechanism of interaction has been confirmed by different spectroscopic data analysis.

IMPLEMENTASI VPN SERVER MENGGUNAKAN PROTOKOL L2TP DAN IPSEC PADA PT. MULTI TERMINAL INDONESIA

This research was conducted with the aim of assisting in the activation and security of hardware and software devices using a VPN L2TP/IPSec server and client network. namely configuring a Mikrotik router network based on VPN L2TP/IPSec server and client, by connecting two public networks into one local network. and the hardware or software devices in question are the SAP financial system, Avaya telephone devices, and CCTV monitoring. By creating a VPN L2TP server and client path, it can help and facilitate employees in working, both in branch areas and in areas outside the PT office. Multi Terminal Indonesia. The analysis method used in this research is the grounded method, where the grounded method itself is carried out by holding data in the field, both in formulating problems, building hypotheses, and drawing research conclusions. Therefore, this research is very dependent on the experience and credibility of the researcher. Keywords: Network, VPN, L2TP, IPSec

Analyzing the Vulnerability of Consumer IoT Devices to Sophisticated Phishing Attacks and Ransomware Threats in Home Automation Systems

This research presents a new and elaborate security model for IoT devices used in home automation systems. The framework comprises five algorithms: The following models were identified: Vulnerability Assessment (VA), Anomaly Detection with Machine Learning (ADML), Behavior Analysis (BA), Intrusion Detection System (IDS), and Adaptive Security Framework (ASF). Ablation study brings out the specificity of each algorithm and underlines the synergy of the algorithms for IoT device protection. Comparisons with similar procedures confirm higher levels of sensitivity and specificity of the proposed method, as well as enhanced efficiency and tunability. Animated charts give crisp information about the total effects of security methods on different parameters. The proposed security framework has therefore been presented as now a viable solution to complex threats and continuous security for the IoT devices used in home automation systems.

Experimental proposal of a 'HALO'-type security device in a FORMULA SAE 2024 type vehicle

This study conducts a numerical and experimental analysis of the use of a "Halo" device, inspired by Formula 1, within the Formula SAE competition. Several alternatives were developed to meet the regulatory requirements of Formula SAE, and the selected proposal was modeled in CATIA V5 using both surface and solid modeling techniques. The model underwent a structural analysis similar to the "Quasi-static Test 1" used for the Formula 1 "Halo," utilizing ANSYS Workbench 2023 R2 for finite element analysis (FEA). Additionally, physical tests were carried out to validate and compare the virtual results, with the aim of assessing the relevance and benefits of this safety device.

Design of Intelligent Protection and Promotion System for Non-Heritage Music Inheritance based on Internet of Things

Eco-friendly methods for the avoidance and control of crop illnesses and insects are essential to minimize synthetic pesticide usage, enhance agriculture quality, safeguard the ecosystem, and foster equitable agricultural growth. Utilizing Internet of Things (IoT) gadgets, the research created a framework for preventing and managing the spread of farming diseases and insects, comprising two primary parts: a plant security device (equipment) and a system for handling information (programs). The research integrated two varieties of protective plant gadgets to accommodate facility- and field-based manufacturing settings, employing ozone sterilizing and light-trap technology. The gadgets were outfitted with many sensors to gather immediate information and track the agricultural growing environment. The database of information features an IoT-based design and incorporates a mobile application to provide remote operation of plant security devices for the competent administration of plant security information. The framework facilitates efficient administration of extensive equipment deployments and multi-device collaboration for illness, pest control, and prevention. The implemented technique has functioned effectively for many years in China and has been utilized for cucumbers, tomatoes, grains, and other commodities. The research illustrates the efficacy and feasibility of the technique in the setting of a greenhouse and the field.

IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.

Spatially Selective Imaging in Color: What You See is What You Want.

Spatially selective imaging (SSI) involves sampling a group of pixels from different positions on an encoded object to display a decoded image. Here, SSI is achieved by using off-axis cylindrical Fresnel lens arrays to decode multiple images from an encoded print of structural color pixels. Each image is optically retrieved by separately placing different "keys" (arrays of lenses in different pseudorandom configurations) over the same encoded print, and then each image is digitally reconstructed for visualization. In addition, a detailed analysis is presented on the designed lenses and pixels, which are all fabricated by two-photon polymerization lithography. Though Fresnel lenses are susceptible to chromatic aberrations, the results show that they can be used for imaging pixels that produce a wide range of colors at low numerical aperture (NA ≈0.2). The images have acceptable color contrast, corresponding to a simulated normalized modulation transfer function (MTF) of ≈0.6 averaged across wavelengths in the visible spectrum. This work can find applications in optical information security devices.

METHOD FOR IMPROVING SECURITY OF IOT DEVICES PAIRING

Background. The usage of IoT technologies leads to the social, technological and financial development of society. While complex systems play an important role in the IoT, the design, integration and use of simple devices really drive the technology's widespread adoption. At the same time, ensuring a high level of security for simple IoT devices is a difficult task. The reasons for this are device limited computing resources and low power consumption requirements. It prevents the implementation of most modern cryptographic protocols for simple IoT devices. From a security point of view, the most critical communication stage is device pairing, when shared encryption keys are formed to establish a secure communication channel. Objective. The purpose of the paper is to analyse the main vulnerabilities of a simple IoT device pairing process and develop a method for improving security of this process. The method should provide proximity-based device authentication and pairing process protection against known attacks, such as man-in-the-middle attack. Methods. The method of pairing process security improvement includes proximity-based device authentication using analysis of the wireless signal strength. The security of the authentication method is proven analytically and by results of practical experiments with measurement of wireless signal strength change with distance and obstacles between devices. Results. Performed research demonstrated that the proposed method guarantees secure user authentication at a close distance between devices and protection against attacker located at least 10 meters from the paired device. Provided theoretical calculations and experimental results show that the level of attacker's wireless signal power increase required for a successful attack exceeds technical capabilities of existing communication devices. Conclusions. The article solves an important issue of improving the security of simple device pairing. The proposed method of proximity-based IoT device authentication provides pairing process protection against man-in-the-middle attacks. Mathematical calculations were confirmed by conducting a number of experiments to research wireless signal power change depending on the distance and types of obstacles between devices. The proposed authentication method can be integrated into the existing JustWorks protocol for connecting simple IoT devices using the BLE communication channel.

Personal data protection in public services

The increasing importance of personal data protection in public services, especially in Indonesia, has become a critical issue due to frequent data breaches involving large organizations. This community service project was conducted to address the growing need for public awareness and implementation of personal data protection measures. The project aimed to educate the public and relevant institutions about the legal frameworks, such as the Personal Data Protection Law (UU No. 27 of 2022), and the importance of safeguarding personal data through digital security measures. The methodology employed included interactive webinars and the dissemination of educational materials focusing on digital device security, password management, and two-factor authentication. The results of the community service showed a significant improvement in participants’ understanding of personal data protection practices, as well as an increased adoption of digital security techniques such as encryption and secure authentication methods. In conclusion, raising public awareness and providing practical solutions to protect personal data are essential steps in mitigating the risks of cyber threats in public services

Zero-day Android botnet detection using neural networks

AbstractAndroid devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security.

Two Novel Semi-Quantum Secure Direct Communication Protocols in IoT.

As Internet of Things (IoT) technology continues to advance, there is a growing awareness of IoT security within the industry. Quantum communication technology can potentially significantly improve the communication security of IoT devices. Based on semi-quantum cryptography and utilizing single photons, this paper introduces two semi-quantum secure direct communication (SQSDC) protocols for use in smart door locks. Protocol 1 is more efficient, and the efficiency analysis shows that the communication efficiency is as high as 28.57%. Security analysis demonstrates the asymptotic security of the protocols, effectively resisting intercept-measure-resend attacks and entangle-measure attacks from potential eavesdroppers. The extended SQSDC protocol (protocol 2) builds upon protocol 1 by enabling a single qubit to transmit two bits of information, resulting in a double efficiency outcome.

Wearable Sensor-Based Behavioral User Authentication Using a Hybrid Deep Learning Approach with Squeeze-and-Excitation Mechanism

Behavior-based user authentication has arisen as a viable method for strengthening cybersecurity in an age of pervasive wearable and mobile technologies. This research introduces an innovative approach for ongoing user authentication via behavioral biometrics obtained from wearable sensors. We present a hybrid deep learning network called SE-DeepConvNet, which integrates a squeeze-and-excitation (SE) method to proficiently simulate and authenticate user behavior characteristics. Our methodology utilizes data collected by wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to obtain a thorough behavioral appearance. The suggested network design integrates convolutional neural networks for spatial feature extraction, while the SE blocks improve feature identification by flexibly recalibrating channel-wise feature responses. Experiments performed on two datasets, HMOG and USC-HAD, indicate the efficacy of our technique across different tasks. In the HMOG dataset, SE-DeepConvNet attains a minimal equal error rate (EER) of 0.38% and a maximum accuracy of 99.78% for the Read_Walk activity. Our model presents outstanding authentication (0% EER, 100% accuracy) for various walking activities in the USC-HAD dataset, encompassing intricate situations such as ascending and descending stairs. These findings markedly exceed existing deep learning techniques, demonstrating the promise of our technology for secure and inconspicuous continuous authentication in wearable devices. The suggested approach demonstrates the potential for use in individual device security, access management, and ongoing uniqueness verification in sensitive settings.

Perception and attitudes of critical care nurses on external urinary catheter securement and the prevalence of securement devices in critically ill patients

Introduction: the literature describes the limited use of urinary catheter securement devices (UC). Critical care nurses (CCNs) consider UC security an important activity for nursing care. Our objective was to determine CCN perceptions and attitudes to external UC security and the prevalence of securement devices in seriously ill patients. Materials and Methods: a cross-sectional observation study was carried out between the CCNs of southern Italy between May and July 2023. All CCNs with a probationary period of less than six months were excluded. Chi-square tests assessed the correlation between variables. The significance level was set to p> 0.05. Results: a total of 77.6% (n=76) know UC securement devices, but only 43.9% (n=43) have these devices available in operational units. CCNs with security devices reported reduced urinary tract infections [(89.5 % n=42) compared to (10.6 % n=5), p=0.007] and patient comfort [(83%, n=60) versus (16.7%, n=12), p=0.02] and dislocation [(84.9%, n=62) versus (15.1%, n=11)]. Changes can be reduced according to CCNs with safety devices [56.2% (n=41) versus 43.8% (n=32), p=0.0001]. Conclusions: the results of this study will significantly contribute to incorporating this activity into standard nursing care and improving the quality of care. Future research should consider the potential effects of nursing care.

Bluetooth Security: A Comprehensive Guide to Protecting Your Wireless Devices

This comprehensive article explores the evolving landscape of Bluetooth security, addressing critical aspects of wireless device protection in an increasingly connected world. The article examines fundamental security threats, including bluejacking, bluesnarfing, bluebugging, and spoofing, while providing detailed analyses of their impact on device security. The article delves into effective device management strategies, emphasizing the importance of visibility settings, pairing protocols, and discovery controls. It presents an in-depth evaluation of protection strategies, incorporating multi-layered security approaches, authentication methods, and software maintenance practices. The article extends to modern monitoring and prevention techniques, highlighting the role of artificial intelligence and machine learning in threat detection. The article also outlines implementation guidelines for organizations and users, focusing on daily security practices, risk mitigation techniques, and user awareness protocols. Additionally, it provides valuable insights into the future of Bluetooth security, discussing emerging threats, quantum computing challenges, and the delicate balance between security measures and usability.

Enhancing Security in Bring Your Own Device Environments (BYOD) with Trusted Platform Modules, Continuous Monitoring, and Hardware Security Modules

Bring Your Own Device (BYOD) policies have transformed organizational workflows, offering flexibility, cost savings, and increased employee satisfaction. However, they also introduce significant security challenges due to device heterogeneity, inconsistent security configurations, and increased attack surfaces. This paper proposes a comprehensive security framework tailored to BYOD environments, integrating Trusted Platform Modules (TPMs) for device attestation, Hardware Security Modules (HSMs) for secure key management, continuous monitoring for real-time threat detection, and dynamic access control to enforce adaptive security policies. Practical use cases, such as securing remote work and employee devices, are discussed, along with challenges and future directions for improving adaptability and cost efficiency in deploying the framework. Keywords: Bring Your Own Device security, Trusted Platform Module, Hardware Security Module, device attestation, continuous monitoring, hardware tampering detection.