Security Features Research Articles

Geofence‐Based Boundary Violation Detection Algorithms in Unmanned Aerial Vehicles

ABSTRACTGeofence is a virtual boundary that helps in location‐based services. Unmanned aerial vehicles (UAVs) fly without a pilot, and geofence helps in regulating these vehicles and constraints a UAV to enter the boundary of other UAV. However, these have to go through many safety and security features like avoiding collision, complying within the restricted area, and organizing airspaces with high traffic. The UAVs need to be within the boundaries to avoid various damages to property and loss of lives. Geofences help to detect boundary violation of UAVs to a great extent. This study uses various mathematical algorithms to create geofences and detect boundary violation of UAVs. The existing methods, Delaunay triangulation and triangle weight characterization with adjacency, are implemented. The existing methods have high execution time as well as high standard deviation and low accuracy. Further, a novel method for boundary violation detection is proposed using a convex hull to create geofences. The results show that the proposed method improves the execution time by 59.23% and accuracy by 1.35%.

Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment

The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.

Integrating IoT and Machine Learning for Advanced Breast Cancer Detection: A Secure Framework for Enhanced Diagnostic Accuracy

Background: Breast cancer continues to be a major global health issue, with outmoded analytic methods often limited in their capacity for early detection. The emergence of telehealth care emphasizes the need for advanced, advanced diagnostic explanations. Objective: This study explores the possibility of integrating advanced technologies, such as thermal imaging cameras, with sophisticated ML algorithms, particularly CNNs, to enhance breast cancer discovery beyond the capabilities of conventional risk models. Method: We propose a pioneering predictive healthcare system that harnesses the IoT and EHRs for real-time, automated breast cancer detection. This system is designed to operate in various healthcare environments, including hospitals, nursing homes, and schools. Beyond early breast cancer detection, it seeks to identify other health issues using the shared rule of DL and IoT techniques. To maintain data integrity and protect patient confidentiality, the system implements strong security protocols, including encrypted data transmission, secure access controls, and adherence to healthcare data protection regulations. Results: The study explores the methodologies and technological frameworks necessary for developing this comprehensive cancer detection system. Emphasis is placed on secure data management practices and the implementation of security protocols to safeguard sensitive health information. Main Findings: This research assesses whether the proposed system can effectively address global challenges related to breast cancer detection and contribute to enhanced patient outcomes. The integration of advanced security features aims to defend patient data and certify the system's reliability and trustworthiness in diverse healthcare settings. Conclusion: This investigate evaluates capabilities of new health care system that combines smart technologies with ML methods to identify breast cancer and possibly other health problems at an early stage. Future investigate should focus on assessing the system’s practical feasibility and effectiveness in real-world scenarios, as well as its ability to uphold stringent security standards. This emphasizes its efficiency when operating on a lightweight and portable processor. Our experimental results indicate that our system achieves a 96.32% accuracy on the BreakHis database and can operate on a Raspberry Pi device with minimal resources.

Opto/Thermoresponsive Multimode Luminescence in Eu- and Er-Activated CaF2 Phosphor for High-Security Anticounterfeiting.

As the landscape of information storage and security continues to evolve, the deployment of sophisticated anticounterfeiting strategies with robust security features and multimodal luminescent capabilities becomes imperative. In this work, Eu3+ and Er3+ ions are codoped into CaF2 phosphors to achieve multimodel optical output. The self-reduction of Eu3+ within the CaF2 matrix gives rise to the coexistence of Eu3+ and Eu2+ ions, which manifests as a color transition from orange to blue as the excitation wavelength is varied from 300 to 335 nm. Moreover, the distinct temperature-dependent behaviors of Eu3+ and Eu2+ ions underscore the material's visible temperature-sensitive luminescence properties, characterized by remarkable thermal sensitivity (Sa = 0.0156 K-1, Sr = 0.83%K-1). Additionally, the strategic introduction of Er3+ ions adds an extra dimension, enabling the realization of color-tunable upconversion luminescence through the fine-tuning of Er3+ concentration. This synergistic integration culminates in the establishment of an efficient three-path authentication model within the CaF2 host matrix, facilitating a dynamic multicolor response to changes in the excitation wavelength and temperature. By harnessing these diverse luminescent modalities, the study delivers a versatile and potent anticounterfeiting solution, advancing the frontiers of information security.

Characteristics analysis and safety evaluation of vehicles turning over long distances

In order to explore the motion and safety characteristics of the U-turn vehicles at long-range U-turn intersections, based on the measured data of the U-turn vehicles at typical locations, we analyzed the characteristics of the U-turn vehicle's trajectories and traffic parameters with the help of probability theory and statistical knowledge, and established the U-turn trajectory model. We used Traffic Conflict Technology (TCT) and take Post Encroachment Time (PET) as the indicator to study the feature of vehicles security of - turn vehicles. Three conclusions are drawn from this study. First, there is a significant difference in the headway between long-range -turn vehicles and those at intersections without opening: the stability of the headway of long-range -turn vehicles is weak, and the release of vehicles is seriously disturbed by the turning flow; Secondly, when turning around at a long interval u-turn intersection, the starting position of vehicle turning is dispersed and the distribution of U-turn trajectories is quadratic distribution. Third, the setting of long interval turning lanes will increase the interference between traffic flows at intersections, thus increasing the number of conflicts with vehicles in the opposite direction. Cross-conflict accounts for the majority of conflicts in long interval U-turn intersections, and rear-end collision is the main conflict type in U-turn intersections without opening. The above research conclusions are helpful to improve the safety of long interval U-turn intersections.

NetSentinel applications for website security

NetSentinel is an extensive web security platform designed to scan, detect and protect websites from potential vulnerabilities and cyber-threats. NetSentinel offers a proactive approach to strengthen and safeguard the website’s security, in an era where data breaches and cyber-attacks are common. The platform analyzes critical security features like SSL security validity, DNSSEC implementation, threat detection, security headers, firewall, and open ports. The main feature of the platform is the scoring mechanism, which assesses the website’s overall security to ensure it is protected from potential threats. The scoring method assigns different weights to the various security features according to their importance in the calculation. This scoring system helps users focus on the most important vulnerabilities and gives clear, practical advice on how to mitigate risks. Along with the main scoring feature the platform also includes 24 other critical security features which are Server location, Whois, DNS Data, DNS Records, Archives, Carbon Footprint, TLS Cipher Suites, TLS Security Configuration, TLS Handshake Simulation, Status, Traceroute, Blocklists, Pages, Social Tags, Headers, HSTS Check, Domain Rank, Tranco Rank, Linked Pages, Mail Services, Redirects, Security Txt, Robots Txt. NetSentinel breaks down complex information into a simple, easy to understand security score, which makes security assessment easier. It also offers clear explanations of the issues and provides step-by-step recommendations on how to fix them, helping users improve their overall security without needing advanced technical knowledge to be possessed. Using this platform, organizations can enhance their site’s ability to withstand cyber threats, ensuring data integrity and secure communication. NetSentinel helps users take control of their web security by balancing technical accuracy with easy-to-use interfaces, making it simple for everyone to stay proactive about their safety online.

Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive Survey

The widespread adoption of encrypted communication protocols has significantly enhanced network security and user privacy, simultaneously elevating the importance of encrypted traffic analysis across various domains, including network anomaly detection. The Transport Layer Security (TLS) 1.3 protocol, introduced in 2018, has gained rapid popularity due to its enhanced security features and improved performance. However, TLS 1.3’s security enhancements, such as encrypting more of the handshake process, present unprecedented challenges for encrypted traffic analysis, rendering traditional methods designed for TLS 1.2 and earlier versions ineffective and necessitating the development of novel analytical techniques. This comprehensive survey provides a thorough review of the latest advancements in TLS 1.3 traffic analysis. First, we examine the impact of TLS 1.3’s new features, including Encrypted ClientHello (ECH), 0-RTT session resumption, and Perfect Forward Secrecy (PFS), on existing traffic analysis techniques. We then present a systematic overview of state-of-the-art methods for analyzing TLS 1.3 traffic, encompassing middlebox-based interception, searchable encryption, and machine learning-based approaches. For each method, we provide a critical analysis of its advantages, limitations, and applicable scenarios. Furthermore, we compile and review key datasets utilized in machine learning-based TLS 1.3 traffic analysis research. Finally, we discuss the main challenges and potential future research directions for TLS 1.3 traffic analysis. Given that TLS 1.3 is still in the early stages of widespread deployment, research in this field remains nascent. This survey aims to provide researchers and practitioners with a comprehensive reference, facilitating the development of more effective TLS 1.3 traffic analysis techniques that balance network security requirements with user privacy protection.

Design of Provably Secure and Lightweight Authentication Protocol for Unmanned Aerial Vehicle systems

Drones also called Unmanned Aerial Vehicles (UAVs) have become more prominent in several applications such as package delivery, real-time object detection, tracking, traffic monitoring, security surveillance systems, and many others. As a key member of IoT, the group of Radio Frequency IDentification (RFID) technologies is referred to as Automatic Identification and Data Capturing (AIDC). In particular, RFID technology is becoming a contactless and wireless technique used to automatically identify and track the tagged objects via radio frequency signals. It also has drawn a lot of attention among researchers, scientists, industries, and practitioners due to its broad range of real-world applications in various fields. However, RFID systems face two key concerns related to security and privacy, where an adversary performs eavesdropping, tampering, modification, and even interception of the secret information of the RFID tags, which may cause forgery and privacy problems. In contrast to security and privacy, RFID tags have very limited computational power capability. To deal with these issues, this paper puts forward an RFID-based Lightweight and Provably Secure Authentication Protocol (LPSAP) for Unmanned Aerial Vehicle Systems. The proposed protocol uses secure Physically Unclonable Functions (PUFs), Elliptic-Curve Cryptography (ECC), secure one-way hash, bitwise XOR, and concatenation operations. We use Ouafi and Phan’s formal security model for analyzing security and privacy features such as traceability and mutual authentication. The rigorous informal analysis is carried out which ensures that our proposed protocol achieves various security and privacy features as well as resists various known security attacks. The performance analysis demonstrates that our proposed protocol outperforms other existing protocols. In addition, Scyther and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool simulation results demonstrates that there is no security attack possible within bounds. Therefore, our proposed LPSAP protocol achieves an acceptable high level of security with the least computational, communication, and storage costs on passive RFID tags.

Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

Understanding critical service factors in neobanks: crafting strategies through text mining

Purpose This study aims to analyse and understand customer sentiments and perceptions from neobanking mobile applications by using advanced machine learning and text mining techniques. Design/methodology/approach This study explores a substantial large data set of 330,399 user reviews available in the form of unstructured textual data from neobanking mobile applications. This study is aimed to extract meaningful patterns, topics, sentiments and themes from the data. Findings The results show that the success of neobanking mobile applications depends on user experience, security features, personalised services and technological innovation. Research limitations/implications This study is limited to textual resources available in the public domain, and hence may not present the entire range of user experiences. Further studies should incorporate a wider range of data sources and investigate the impact of regional disparities on user preferences. Practical implications This study provides actionable ideas for neobanking service providers, enabling them to improve service quality and mobile application user experience by integrating customer input and the latest trends. These results can offer important inputs to the process of user interaction design, implementation of new features and customer support services. Originality/value This study uses text mining approaches to analyse neobanking mobile applications, which further contribute to the growing literature on digital banking and FinTech. This study offers a unique view of consumer behaviour and preferences in the realm of digital banking, which will add to the literature on the quality of service concerning mobile applications.

Advanced Deep Learning Techniques for Information Security Vulnerability Detection Using Machine Learning

The increasing rate on the complexity and amount of security threats demand more advanced information security vulnerability detection techniques. Traditional methods have limited capability to respond to such diverse and evolving threats in real-time. This paper presents a complete solution by using new machine learning models in combine with advanced mathematical approaches to organize the early detection and prediction of vulnerabilities that later content will be about the information security systems. We investigate the accuracy of different models (with an emphasis on modern approaches that go beyond the standard SVM, CNN, and GNN). It initiates with Bayesian Networks that use probabilistic graphical models for showing the interactions of diverse security characteristics to support reasoning about vulnerabilities as a function on the conditional dependencies. Decision Trees and ensemble techniques like Extreme Gradient Boosting (XGBoost) are able to deal with both heterogeneous data types and are more capable of modelling complex interaction between security features. For unsupervised cases, we use things like Isolation Forests for anomaly detection and Gaussian Mixture Models (GMM) to detect rare patterns in network packets that may be a sign of security attacks. We further investigate the possibility of reinforcement learning (i.e., Q-Learning) to continue to evolve with network changes and identify threats in real-world threatening environment. The learning process is streamlined and the interpretability of results is improved by integrating mathematical techniques such as Markov Chains (for modelling probabilistic transitions within network states) and optimization methods like Lasso Regression (for feature selection). In addition to this, the approach explores Autoencoder-based models but most specifically Variational Autoencoders (VAEs) for their unsupervised learning ability in identifying rare and new zero-day vulnerabilities. Extensive experiments on a variety of cybersecurity datasets demonstrate the effectiveness and efficiency of our approach, where substantial enhancements in detection speed and accuracy are achieved. The cloud-based cybersecurity model introduced in this research, backed by advanced machine learning models and mathematical frameworks could potentially serve to lay a foundation for the future of real-time cyber security defence mechanisms.

Regularity through randomness: Bioinspired quasi-amorphous photonic structures for multilevel information encryption

The biological world has produced a wealth of complex photonic materials offering abundant design inspirations. However, creating these intricate photonic structures intertwining order, disorder, and hierarchical arrangements remains challenging. Achieving this necessitates nano-fabrication technologies capable of integrating intended macroscopic properties while accommodating microscopic variability or randomness, akin to the operational principles observed in biological organisms. Here, we present quasi-amorphous photonic structure (QAPS) patterns with regulated sophisticated visual effects through hierarchically distributions of photonic elements with engineered randomness. Enabled by an infiltration-assisted (IFAST) inkjet printing method, the visual appearances of QAPS patterns are precisely controlled by collective harnessing of nanoscale resonant scattering of colloids, mesoscale interferences, and macroscale light transporting. The QAPS patterns exhibit isotropic diffuse colors, unprecedented iridescence and enable multichannel encrypted information that can be selectively identified using specific filters at particular specular illumination angles. Additionally, the intricate infiltration fields confer high adjustability in colloidal assembly, offering sophisticated packing geometries and color distributions at the microscale, which can serve as physical unclonable security features. The exceptional structural complexity and optical versatility of QAPSs enable the integration of multilevel security features within a single system, providing a promising roadmap to achieve larger information capacity, multichannel cryptography, and enhanced encryption levels.

A network security protection scheme for tax system based on elliptic curve cryptography

With the rapid development of modern information technology and network technology, the construction of tax informatization plays an important role in improving the level of tax management and work efficiency. However, in the centralized data processing mode, the establishment of large data center and the efficient, stable, and secure connection of server bandwidth have become key challenges. Moreover, the openness and interconnectivity of the tax system network pose various network security threats, seriously threatening the integrity and security of data. For the above issues, this article proposes a tax system network security protection scheme based on elliptic curve cryptography. This scheme combines hash function and elliptic curve cryptography to achieve user anonymity and secure generation of session keys, effectively ensuring the secure transmission of confidential information. The security and effectiveness of the scheme are verified through informal and formal security analysis based on security proof, BAN logic and AVISPA tool. And a comprehensive evaluation of the scheme is conducted, and the results show that the scheme significantly reduced the costs while providing security features.

ACTIVITIES OF STATE AND NON-STATE ENTITIES IN THE FUNCTIONING SYSTEM OF UKRAINE’S INFORMATION SECURITY

The essential characteristics of information security are analyzed through the activities of state and non-state entities. It is noted that the main task of the state is to create and maintain an institutional mechanism for ensuring information security. The leading role of non-state entities such as analytical centers and mass media in the functioning of the information security system of democratic countries is substantiated.

WhatsApp: A Blessing or Bane for Communication between Emergency Physicians and Vascular Surgeons?

Abstract The integration of WhatsApp (WhatsApp Inc., Mountain View, CA, USA) into the health-care sector is transforming the dynamics of medical communication. Launched in 2009 and incorporated into the Facebook suite of companies in 2014, WhatsApp has become a pivotal platform for health-care professionals. It allows for quick, personal, and group messaging, which is increasingly used to exchange patient information, discuss cases, and make timely decisions. The app’s end-to-end encryption is critical for maintaining patient confidentiality, ensuring that sensitive data are only accessible to the intended recipients. This security feature and the group sharing format enable health-care teams to communicate confidentially and efficiently, bolstering the oversight capabilities of attending senior consultants or physicians over their junior peers. The real-time exchange of information is crucial in providing continuous supervision and facilitating immediate feedback, which is indispensable in clinical training and patient care. However, the widespread adoption of WhatsApp also comes with challenges and potential drawbacks, which have been the focus of past research. These studies assessed the impact of WhatsApp on health-care delivery, considering both the enhancement of communication it offers and the issues it may present, such as data security beyond encryption and the blurring of professional and personal boundaries. In this “medical opinion” from an academic tertiary care center in Muscat, we explored how emergency medicine physicians can effectively use WhatsApp to expedite or bypass referral processes for emergency cases, particularly those involving vascular and other surgical emergencies.

A Survey of Hardware Improvements to Secure Program Execution

Hardware has been constantly augmented for security considerations since the advent of computers. There is also a common perception among computer users that hardware does a relatively better job on security assurance compared with software. Yet, the community has long lacked a comprehensive study to answer questions such as how hardware security support contributes to security, what kind of improvements have been introduced to improve such support and what its advantages/disadvantages are. By generalizing various security goals, we taxonomize hardware security features and their security properties that can aid in securing program execution, considered as three aspects, i.e., state correctness, runtime protection and input/output protection. Based on this taxonomy, the survey systematically examines (1) the roles: how hardware is applied to achieve security; and (2) the problems: how reported attacks have exploited certain defects in hardware. We see that hardware’s unique advantages and problems co-exist and it highly depends on the desired security purpose as to which type to use. Among the survey findings are also that code as part of hardware (aka. firmware) should be treated differently to ensure security by design; and how research proposals have driven the advancement of commodity hardware features.

Cyber Insecurity For Internet Banking

Internet banking has become a crucial aspect of modern financial transactions, enabling customers to access their account information and perform various transactions from anywhere. Ensuring the security of these transactions is one of utmost importance, and this is where authentication protocols come into play. One such protocol is Kerberos, which is widely used for secure authentication in many applications, including internet banking. In this paper we discuss the basic operations and functionalities of Kerberos while pinpointing the advantages of using Kerberos as an authentication protocol in internet banking. We also discuss the security features of using Kerberos, including its use of encrypted tickets and its ability to prevent replay attacks

Java Development across Industries: Transforming Healthcare, Retail, and Agriculture

Java, a versatile and widely-adopted programming language, is transforming critical industries such as healthcare, retail, and agriculture. This article explores how Java developers leverage the language's robustness, platform independence, and extensive ecosystem to create innovative solutions that address industry-specific challenges. In healthcare, Java's security features and scalability enable the development of secure and interoperable Electronic Health Record systems. Retail benefits from Java's cross-platform compatibility and performance in building omnichannel platforms that integrate online and offline experiences. In agriculture, Java's IoT capabilities and big data integration revolutionize farming practices through precision agriculture and data-driven decision-making. Case studies in each industry demonstrate Java's significant impact on key metrics such as patient data accessibility, online-to-offline conversions, and crop yields. As these sectors continue to evolve, Java's adaptability positions it as a key driver of future advancements.

Analysis of Opportunities and Challenges of Blockchain Technology in the Islamic Banking Industry (Case Study on the Use of Smart Contracts)

Purpose: This research concludes that, with a strategic and collaborative approach, blockchain technology can be an effective tool for transforming the Islamic banking industry. Methodology: This study uses qualitative methods with literature analysis from 19 journals, one book, and three websites to explore how this technology can be applied in risk management, asset tracking, and interbank transactions. Results: The research results reveal that this technology can reduce fraud, manipulation, and cyberattacks, as well as increase customer and sharia authority trust through transaction transparency. However, there are major challenges to overcome, such as immature regulatory aspects, the need for a strong infrastructure, and a lack of understanding of this technology among customers. This study also emphasizes the importance of collaboration between Islamic banking, regulators, and other stakeholders to maximize the potential of blockchain technology. Limitations: Case studies on the use of smart contracts in the Sharia banking industry show that this technology can increase efficiency and Sharia compliance, reduce administration costs, and simplify transactions. Contribution: This study describes the role of blockchain in the sukuk issuance process. Blockchain technology, which is known for its decentralization, transparency, and security characteristics, has great potential to increase efficiency and transparency in Sharia banking operations.

EVALUASI KINERJA DAN EFISIENSI JARINGAN LORAWAN UNTUK APLIKASI IOT DI LINGKUNGAN SUBURBAN

The current era of digitalization is rapidly advancing, especially in the utilization of Internet of Things (IoT) technology for various purposes, including in the transportation and vehicle rental sectors. This research focuses on the application of the LoRaWAN network in supporting the electric scooter rental system in suburban environments, particularly in the Kintamani area. The designed system allows real-time monitoring of electric scooter locations through the integration of GPS modules, and also provides additional security features with an emergency button for urgent situations. The system is also equipped with rental time settings to maximize efficiency in managing electric scooter rentals. LoRaWAN was chosen for its ability to transmit data over long distances with low power consumption, which is highly suitable for suburban areas with wide coverage and limited infrastructure. This research not only evaluates the network's performance in terms of communication but also reviews the system's efficiency in terms of the number of devices that can be connected simultaneously. The evaluation focuses on how optimally this system can be used on a larger scale, especially in supporting the growing needs of IoT. This study provides insights into the potential development of a safer and more efficient IoT-based bike rental system in suburban environments.