Authentication Mechanisms for ONC RPC

The technological development of artificial intelligence, Internet of Vehicles, big data, edge computing, intelligent manufacturing, 5G and other technologies will bring great prospects to the popularization of the Internet of Things. In the future, we will see more and more IoT systems of different sizes. It is particularly important to ensure the control of IoT system message transmission, ensure system security, and monitor IoT systems. The Internet of Things protocol and its system node authentication mechanism have great research value. This paper analyzes the MQTT protocol and its security authentication mechanism, and designs and implements an identity authentication mechanism based on historical information nodes. The proposed authentication mechanism can effectively prevent forgery attacks, counterfeit attacks, replay attacks, and Sybil attacks.

The increasing reliance on web applications across domains such as e-commerce, banking, healthcare, and enterprise systems has significantly amplified the importance of secure and reliable authentication mechanisms. Traditional password-based authentication methods are increasingly vulnerable to modern cyber threats, including phishing, brute force attacks, and credential stuffing, thereby necessitating the adoption of more advanced and robust authentication strategies. This study presents a comprehensive comparative analysis of modern authentication mechanisms in web applications, including Multi-Factor Authentication (MFA), OAuth 2.0, JSON Web Tokens (JWT), Single Sign-On (SSO), and biometric-based authentication systems. The research evaluates these mechanisms based on critical parameters such as security strength, usability, scalability, performance, and implementation complexity. It further examines architectural models, token-based frameworks, and federated identity systems that support secure user authentication in distributed environments. Additionally, the study reviews common vulnerabilities, threat mitigation techniques, and real-world deployment scenarios to assess the effectiveness of each approach. The findings highlight a growing shift toward hybrid authentication models that integrate multiple techniques to achieve enhanced security and user experience. However, challenges remain in balancing security with usability, ensuring interoperability across platforms, and maintaining privacy in decentralized systems. The paper concludes by identifying future research directions focused on passwordless authentication, zero-trust security models, and adaptive authentication systems for next-generation web applications.Keywords : Authentication mechanisms, Web application security, Multi-Factor Authentication (MFA), OAuth 2.0, JSON Web Tokens (JWT), Single Sign-On (SSO), Biometric authentication, Passwordless authentication, Zero Trust security, Cybersecurity

Wireless Sensor Networks are being widely used in many real time applications that require continuous monitoring and maintenance of surroundings and their environment. Communication is a vital operation in WSN and most of the times the data is confidential. Hence, security of data is a major concern in WSN and it has been observed that a secure authentication mechanism will ensure confidentiality and integrity of data in WSN. Many authentication mechanisms have been designed but most of them require the sensor nodes to have much computational resources, involves much communication and storage overheads and are quite complex. In this paper, we propose a simple, secure, dynamic, scalable, efficient and lightweight protocol for mutual authentication of nodes in WSN. The protocol is based on tokens, each sensor node acquires a token from base station (BS) and later on without any involvement of BS sensor nodes mutually authenticate each other using acquired tokens. The proposed protocol was modeled in scyther for verification and no potential attacks were detected.

Enterprise Java applications form the backbone of many mission-critical systems in sectors such as healthcare, finance, and government. With increasing security threats targeting these applications, a comprehensive understanding of Java-based security frameworks and authentication mechanisms is essential. This paper presents an in-depth exploration of contemporary security frameworks in the Java ecosystem, such as Spring Security, Apache Shiro, and Jakarta Security. It also analyzes various authentication mechanisms, including Basic Auth, OAuth2, JWT, SAML, and mutual TLS (mTLS), and their suitability for enterprise deployment. Furthermore, the paper delves into threat modeling techniques such as STRIDE and OWASP Top 10 vulnerabilities relevant to Java applications. A case study of a Hospital Management System (HMS) demonstrates the practical application of layered security strategies using modern frameworks and secure coding practices. The paper concludes with a discussion on emerging challenges, such as API-level threats, and offers future research directions. The goal is to provide a holistic reference for developers, architects, and security analysts working on Java enterprise applications.

Broadcast Communication is crucial in VANET communication, to send and receive safety messages within network. Securing these beacon message is a challenge, since they are very prone to clone and Sybil attacks. Many works have been proposed to address this problem but they failed to address how to detect and protect these messages from clone attacks and also limited to static networks with limited data sizes. To achieve this a secure authentication and attack detection mechanism can be designed. In this paper we propose a secure broadcast message authentication and attack detection mechanism with Identity – Based Signatures. Experimental results proved that it can be used in both V2V and V2RSU c communications. Our scheme shown best performance compared to existing schemes in terms of packet delivery ration, detection rate and detection time.

Due to the rapid growth in sensor technology and embedded technology, wireless body area network WBANs plays a vital role in monitoring the human body system and the surrounding environment. It supports many healthcare applications on the one hand and are very much help full in pandemic scenarios. It has become the most innovative health care area, which is intriguing to many researchers because of its vast future prospective and potential. Data collected by different wireless sensors or nodes is very personal, critical, and important because of human life involvement. WBANs can minimize human to human contact, which helps stop the spread of severe infectious diseases. The biggest concern is the maintenance of privacy and accuracy of data is still a hot area of research due to nature of attacks, which are changing day by day and increasing, as well as for the sake of better performance. A suitable security mechanism is a way to address above issues, for achieving data security, it is expedient to propose a mechanism. It is essential to update the patient’s regular data. WBANs help to deliver truthful reports related to the patient’s health regularly and individually. This paper proposes an algorithm that shows a better result than the existing algorithm in their previous works. This work is all about proposing a mechanism which needs comparatively less resource. Only authentic entities can interact with the server, which has become obligatory for both sides, keeping data safe. Several authentication schemes have been proposed or discussed by different researchers. This paper has proposed a Secure and Efficient WBANs Authentication Mechanism (SEAM). This security framework will take care of the authentication and the security of transmitted data.

The metaverse is a new virtual world that has the potential to significantly impact our interactions with digital content and with each other. It is a shared virtual environment where users can seamlessly and with immersive experiences create, interact, and enjoy digital assets. Nevertheless, the metaverse also poses fundamental challenges, particularly about security and privacy concerns, that require careful consideration. One of the most daunting aspects of securing the metaverse is authentication. Several solutions have been proposed, including deployment of blockchain technology and smart contracts, to address these authentication challenges. While these methods provide a secure and tamper-proof authentication mechanism, they fail to meet certain critical security and privacy requirements like interoperability and decentralization. This research proposes an enhanced privacy-preserving authentication scheme based on blockchain, elliptic curve cryptography, biohashing, and a physical unclonable function that guards against various attacks. The proposed scheme does not rely on a single central authority and consists of various phases, including user and avatar authentication, password change, and avatar generation phases. The proposed scheme underwent security assessment using the Burrows Abadi Needham (BAN) logic, ProVerif tool, and Scyther tool. The results demonstrate that it provides a better level of security against a wide range of attack vectors. The proposed scheme offers a swift and efficient authentication mechanism that adheres to the requirements of the metaverse environment, such as interoperability, decentralization, and privacy protection, and requires less computation cost as compared to state-of-the-art schemes.

Handwritten signature verification is a critical aspect of personal authentication in numerous sectors, including banking, legal, and access control systems. This paper provides a comprehensive investigation into writer-dependent signature verification techniques, spanning from traditional approaches such as Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to cutting-edge deep learning architectures like Siamese and triplet networks. Through extensive experimentation and meticulous analysis conducted on benchmark datasets, we rigorously compare and evaluate the efficacy of these approaches. Our study reveals intriguing insights into the performance characteristics of different methodologies, shedding light on their strengths, weaknesses, and areas for improvement. Notably, we observe that while traditional feature-based methods excel in capturing discriminative information from signature images, deep learning architectures offer enhanced flexibility and adaptability, particularly in scenarios with large and diverse datasets. By combining the strengths of both paradigms, we demonstrate the potential for building robust and reliable signature verification systems capable of handling real-world challenges and variations. We highlight the importance of carefully curated datasets and well-designed network architectures in achieving optimal results, thereby providing valuable guidelines for practitioners and researchers in the field. In conclusion, this study contributes significant insights to the advancement of handwritten signature verification systems, emphasizing the importance of leveraging both traditional methodologies and modern deep learning techniques for building secure and dependable authentication mechanisms. By addressing key challenges and exploring novel approaches, our research aims to foster innovation and enhance the security measures associated with handwritten signature authentication in diverse applications. Keywords—Handwritten signature verification, Writer-dependent approach, Feature-based methods, Deep learning, Siamese networks, Triplet networks, Benchmark datasets, Performance evaluation, Authentication mechanisms, Security measures.

Blog services become popular in recently years. Web users can post their articles through the services. On the other hand, the users do not have full control of the articles after posted while the services are built with client/server model. Therefore, we propose to use peer-to-peer (P2P) technology to establish blog services. However, the anonymous feature disables the P2P technology to conduct user identification. The OpenID provides secure and unified authentication mechanism to improve the anonymity. In addition, the OpenID has the single sign on procedure to reduce redundant, multiple accounts and passwords. The paper presents a blog service platform which integrates the P2P networks and the OpenID authentication mechanism together. Therefore, user scan establish their own blogs easily with full control. The platform can be utilized for other Internet applications as well.

Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques.

In modern software engineering, API (Application Programming Interface) is widely used to develop applications rapidly by reusing data structure, frameworks, class libs, and etc. However, due to the considerable number of interfaces, lack of documents and timely maintenance and updates, APIs are often used in a wrong way. Therefore, it has become an important problem to detect API misuse in an automatic way. Many existing automatic API detecting methods do not make full use of APIs’ potential semantic information and independent integrity of each API. In this paper, we employ Stacked LSTM to learn the API usage specification to detect the API misuse defects. Specifically, first, we obtain ACSG (API Call Syntax Graph) through the static analysis of source code. And then, based on ACSG, we generate API sequences, and transform the sequences into for training. Third, in order to represent the APIs in a semantic way, we apply word2vec as a pre-training model to embed features of each API. Though the stacked LSTM model, we regard embedding precious API sequence as the input to model the API use specifications and discover the potential API misuse defects by judging whether the next API is in the output (API probability list) or not. We design experiments to evaluate the effectiveness our method with Java Cryptography APIs and their used code, and the results show the advancement of our proposed method.

The distinguishing feature of a metasystem is middleware that facilitates viewing a collection of large, distributed, heterogeneous resources as a single virtual machine, where each user of the metasystem is identified by a unique metasystem-level identity.The physical resources of the metasystem can exist in multiple administrative domains, each with different local security requirements and authentication mechanisms (e.g., Kerberos, publickey).The problem this paper addresses is how to map the metasystems-level identity to an appropriate account on each local physical machine for the purposes of process creation, such that the access control and authentication policies of each local machine are not violated.This mapping must ensure the integrity of the local machines, must ensure the integrity of the metasystem user's data, and must not unnecessarily burden either the metasystem users, the metasystem system administrator, or the local machine system administrators.Specific examples are drawn from experiences gained during the deployment of the Legion metasystem.For example, Legion configurations for local sites with different access control mechanisms such as standard UNIX mechanisms and Kerberos are compared.Through analysis of these configurations, the inherent security trade-offs in each design are derived.These results have practical importance to current and future metasystem users and to sites considering any future inclusion of local resources in a global virtual computer.

The security threats to the personal information of Internet users raise the need for improved security measures and authentication mechanisms of username and password so that intruders can be discouraged. Another problem faced by modern-day Internet users is that they are required to maintain a long list of passwords for their web accounts maybe 15-20 accounts. Therefore, this raises the need to introduce a better and more reliable authentication mechanism which is not dependent on a series of characters, but rather on a technology that is unique and only possessed by the individual. However, there are some secure services that revolve around the distributed sharing of data, and do not provide any log-in facilities for any website or web account with high level of security, accessibility and usability. This paper presents an answer to all these problems in a single solution, named FingerID, by focusing the theoretical background of the subject and an in-depth review of the literature available on various aspects of security provisions used in the industry for distributed systems. During the course, the objective is to identify clear criteria which would enable robust comparison between FingerID and various similar applications available in the market.

Context: From the past few years, Application Programming Interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects to achieve their development goals efficiently using APIs; however, with the rapid increase in the number of APIs, the manual selection of Mashup-oriented API is becoming more difficult for the developer. Objective: In the COVID-19 pandemic, everyone wants an update about the latest Standard Operating Procedures (SOPs) and the latest information on COVID-19. Additionally, a software developer wants to develop an application that provides the SOPs and latest information of COVID-19; a developer can add these functionalities into an application using COVID-19-based APIs. Moreover, the current work aims at proposing a COVID-19-based API recommendation system for the developers. Method: In this study, we propose a COVID-19-based API recommendation system for developers. The recommendation system takes a developer query as input and recommends top-3 APIs and supported features, which help the developer during software development. Furthermore, the proposed COVID-19-based API recommendation system ensures the maximum participation of the developers by validating the recommended APIs and recommendation system from the expert developers using research questionnaires. Results: Additionally, the proposed COVID-19-based API recommendation system’s output is validated by expert developers and evaluated on 120 expert developers’ queries. In addition, experiment results show that single value decomposition achieves better prediction. Conclusion: We conclude that it is significantly important to recommend APIs along with supported features to the developer for project development, and future work is needed to take more developer’s queries also to build Integrated Development Environment for the developers.

While Application Programming Interface (API) enables an easy and flexible software development process, selecting a best-fit API is often non-straightforward in practice due to misunderstanding on the API specification or a complex programming context etc.. Consequently, the API selection has always been time-consuming and error-prone. In recent years, API recommendation systems have been introduced to help developers choose an API automatically, e.g., Eclipse and IntelliJ can generate an internal or user-defined API on the fly. Other research leveraged language models to capture the regularity in API usage and further guide the completion of APIs. While existing approaches provided a general support for API usage, they suffer from the lack of semantic awareness (e.g., Eclipse) and code relevance (e.g., language model based methods). To overcome these limitations, we proposed Crmac in this paper. The key insight of Crmac is a combination of a cache language model which learns code regularity from both open-source projects and local projects, as well as a relevance mining engine that identifies similar code to enable a weighted language model training. In our empirical evaluation, Crmac overwhelmed n-gram approaches, with an improvement of 5.28% in terms of top 10 accuracy. Moreover, over 79% APIs were correctly predicted in the top 10 guesses of Crmac.