Two-factor Research Articles

How many FIDO protocols are needed? Analysing the technology, security and compliance

To overcome the security vulnerabilities caused by weak passwords, thus bridge the gap between user friendly interfaces and advanced security features, the Fast IDentity Online (FIDO) alliance defined a number of authentication protocols. The existing literature leverages all versions of the FIDO protocols, without indicating the reasons behind the choice of each individual FIDO protocol (i.e., U2F, UAF, FIDO2). Inevitably, the question “which protocol is more suitable per case” becomes significant. To provide an answer to the previous question, this article performs a thorough comparative analysis on the different protocol specifications and their technological and market support, to identify whether any protocol has become obsolete. To reach to a conclusion, the proposed approach (i) explores the existing literature, (ii) analyses the specifications released by the FIDO Alliance, elaborating on the security characteristics, (iii) inspects the technical adoption by the industry and (iv) investigates the compliance of the FIDO with standards, regulations and other identity verification protocols. Our results indicate that FIDO2 is the most widely adopted solution; however, U2F remains supported by numerous web services as a two-factor authentication (2FA) choice, while UAF continues to be utilised in mobile clients seeking to offer the Transaction Confirmation feature.

A new three-factor authentication scheme using Chebyshev chaotic map for peer-to-peer Industrial Internet of Things

The Industrial Internet of Things (IIoT) is an emerging technology for smart manufacturing and productivity improvement. Due to the increasing complexity of manufacturing, devices with smart industrial sensors from several domains work together on the same job, raising serious security and privacy concerns about peer-to-peer (P2P) communication. Specifically, P2P secure communication should be focused on when it comes to high efficiency and security of information exchange between two industrial sensors. However, none of the existing schemes can resist physical attacks towards industrial sensors. To resolve the problem, we propose a new three-factor authentication and key agreement scheme named NTFAK for P2P IIoT communication using Physical Unclonable Functions (PUF) and Chebyshev chaotic mapping, where PUF is used to replace the smart card factor in the traditional schemes. The unclonable property of PUF and its ability to securely identify a device by challenge-response can provide safety to physical attacks. Besides, the use of the Chebyshev chaotic map also can provide high computational efficiency while ensuring better security. The proposed scheme enables secure communication between any two industrial sensors, which can resist physical attacks and offer a smaller key size, faster computation, and higher efficiency for IIoT. The security of our scheme is formally proved by the random oracle model, and security properties are discussed to show our scheme resists various attacks. Moreover, experiments were implemented to prove the efficiency of our scheme. Specifically, the results show that NTFAK can improve the computation and communication overheads by 36.6% and 17.5% at least, compared to existing schemes, while also ensure a minimum time reduction of 30.4% in delay time. In terms of security and functionality features, NTFAK satisfies the proposed 14 properties, outperforming existing solutions.

Decentralized Identity Authentication Mechanism: Integrating FIDO and Blockchain for Enhanced Security

FIDO (Fast Identity Online) is a set of network identity standards established by the FIDO Alliance. It employs a framework based on public key cryptography to facilitate multi-factor authentication (MFA) and biometric login, ensuring the robust protection of personal data associated with cloud accounts and ensuring the security of server-to-terminal device protocols during the login process. The FIDO Alliance has established three standards: FIDO Universal Second Factor (FIDO U2F), FIDO Universal Authentication Framework (FIDO UAF), and the Client to Authenticator Protocols (CTAP). The newer CTAP, also known as FIDO2, integrates passwordless login and two-factor authentication. Importantly, FIDO2’s support for major browsers enables users to authenticate their identities via FIDO2 across a broader range of platforms and devices, ushering in the era of passwordless authentication. In the FIDO2 framework, if a user’s device is stolen or compromised, then the private key may be compromised, and the public key stored on the FIDO2 server may be tampered with by attackers attempting to impersonate the user for identity authentication, posing a high risk to information security. Recognizing this, this study aims to propose a solution based on the FIDO2 framework, combined with blockchain technology and access control, called the FIDO2 blockchain architecture, to address existing security vulnerabilities in FIDO2. By leveraging the decentralized nature of the blockchain, the study addresses potential single points of failure in FIDO2 server centralized identity management systems, thereby enhancing system security and availability. Furthermore, the immutability of the blockchain ensures the integrity of public keys once securely stored on the chain, effectively reducing the risk of attackers impersonating user identities. Additionally, the study implements an access control mechanism to manage user permissions effectively, ensuring that only authorized users can access corresponding permissions and preventing unauthorized modifications and abuse. In addition to proposing practical solutions and steps, the study explains and addresses security concerns and conducts performance evaluations. Overall, this study brings higher levels of security and trustworthiness to FIDO2, providing a robust identity authentication solution.

Lung Cancer Prediction and Classification Using Decision Tree and VGG16 Convolutional Neural Networks

Introduction A malignant abnormal growth that starts in the tissues of the lungs is called Lung Cancer. It ranks among the most common and lethal cancers globally. Lung Cancer is particularly dangerous because of its aggressive nature and how quickly it can extend to other areas of the body. We propose a two-step verification architecture to check the presence of Lung Cancer. The model proposed by this paper first assesses the patient based on a few questions about the patient's symptoms and medical background. Then, the algorithm determines whether the patient has a low, medium, or high risk of developing lung cancer by diagnosing the response using the “Decision Tree” classification at an accuracy of 99.67%. If the patient has a medium or high risk, we further validate the finding by examining the patient's CT scan image using the “VGG16” CNN model at an accuracy of 92.53%. Background One of the key areas of research on Lung Cancer prediction is to identify patients based on symptoms and medical history. Its subjective nature makes it challenging to apply in real-world scenarios. Another research area in this field involves forecasting the presence of cancer cells using CT scan imagery, providing high accuracy. However, it requires physician intervention and is not appropriate for early-stage prediction. Objective This research aims to forecast the severity of Lung Cancer by analyzing the patient with a few questions regarding the symptoms and past medical conditions. If the patient has a medium or a high risk, we further examine their CT scan, validate the result and also predict the type of Lung Cancer. Methodology This paper uses the “Decision Tree” algorithm and the Customised “VGG16” model of CNN for the implementation. The “Decision Tree” algorithm is used to analyze the answers given by the patient to distinguish the severity of Lung Cancer. We further use Convolution Neural Networks with a Customised “VGG16” model to examine the patient's CT scan image, validate the result and categorize the type of Lung Cancer. Results The “Decision Tree” approach for forecasting the severity of lung cancer yields an accuracy of 99.67%. The accuracy of the customized “VGG16” CNN model to indicate the type of Lung Cancer suffered by the patient is 92.53% Conclusion This research indicates that our technique provides greater accuracy than the prior approaches for this problem and has extensive use in the prognosis of Lung Cancer.

Lightweight blockchain-based remote user authentication for fog-enabled IoT deployment

The Internet of Things (IoT) seeks to enhance human life by embedding everyday objects with intelligence. As deployed in diverse environments, IoT devices exchange substantial data to offer autonomously smart and innovative services. Cloud computing serves as an effective solution for processing and storing data from IoT devices, enabling remote access for end-users. However, authenticating remote users poses a critical challenge. Although several existing schemes tackle this challenge, they have notable security and performance weaknesses. This paper introduces a lightweight, distributed multi-factor authentication scheme for remote users in IoT. The proposed approach, named Lightchain, integrates blockchain technology and fog computing while incorporating a lightweight cryptographic hash function. The security of Lightchain is rigorously confirmed through formal verification using the well-recognized Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Additionally, we develop and assess Lightchain’s functionality using the solidity language across diverse use cases. A comparative analysis is presented to evaluate performance costs and security requirements against recent methods. The proposed scheme demonstrates efficiency and robustness, making it well-suited for a range of IoT applications.

FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication

FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication

Security Analysis on a Three-Factor Authentication Scheme of 5G Wireless Sensor Networks for IoT System

Security Analysis on a Three-Factor Authentication Scheme of 5G Wireless Sensor Networks for IoT System

Impact Of Web (URL) Phishing and Its Detection

Abstract: Web phishing is a persuasive and evolving cyber threat that poses significant risks to individuals, businesses, and organizations in the modern digital age. This paper aims to provide an overview of web phishing, focusing on its methods, detection techniques, and prevention. Phishing attacks occur when malicious actors use deceptive practices to trick people into divulging sensitive or classified information such as passwords, credit card details, or personal data. These attacks primarily manifest through emails, websites, or social engineering tactics. Phishing emails often impersonate trusted entities and lure recipients into clicking on malicious links or downloading harmful attachments. Web phishing involves using fraudulent websites that mimic legitimate ones to steal user information or deliver malware. Detecting web phishing attacks is an ongoing challenge due to the sophistication of attackers. Several detection techniques have been developed, including heuristic analysis, machine learning algorithms, and real-time URL analysis. These methods analyze various attributes of websites and emails to identify suspicious patterns or behaviors. Detecting web phishing is equally essential. Effective prevention strategies include user education and awareness programs, using two-factor authentication, regular software updates, and deploying advanced email filtering and anti-phishing tools. User training is crucial in helping individuals recognize phishing attempts and avoid falling victim to them.

A Study on the Navigating Cybersecurity Challenges in the Digital-era

In today's era Navigating cybersecurity concerns is a top priority for individuals, corporations, and governments in today's digital landscape. This in-depth research explores the complex realm of cybersecurity, looking at the new dangers and the methods used to successfully counter them. The swift growth of digital technologies, encompassing cloud computing and the widespread use of Internet of Things devices, has greatly expanded the attack surface, revealing weaknesses that malevolent actors can promptly take advantage of. Gaining an understanding of these weaknesses is essential to creating strong defenses. The present study centers on pivotal components of cybersecurity strategy, commencing with the pivotal function of encryption in safeguarding confidential information. An essential layer of security is provided by encryption methods and protocols, which prevent unauthorized users from reading data. Furthermore, the application of multi-factor authentication is examined as a crucial security precaution. The security of digital assets is greatly increased by MFA, which requires users to submit several kinds of authentication, such as passwords, biometric information, or security tokens. The study also explores the significance of intrusion prevention systems and intrusion detection systems in preventing unwanted access. IDS keeps an eye on networks for unusual behavior or security policy violations and instantly notifies administrators of any hazards. people can learn how to spot and avoid social engineering scams. Organizations may greatly lower the chance of successful cyber intrusions by arming users with knowledge. Finally This report concludes by emphasizing how urgent it is to address cybersecurity issues in the digital age. A proactive, multi-layered protection plan is essential due to the increasing sophistication and pervasiveness of cyber threats.

ABCD Analysis of Voice Biometric System in Banking

Purpose: This article aims to provide an overview of the voice biometric system in banking and its benefits using the ABCD analytical methodology. Voice biometric systems in banking are pivotal for improving customer experiences, effortlessly integrating into various service channels like call centers and mobile apps. This article provides advantages, benefits, constraints and disadvantages (ABCD) analysis of the voice biometric offerings for banking sector. Design/Methodology/Approach: The research examined published papers through the ABCD analysis framework, employing quantitative analysis within focus group interactions to uncover crucial attributes and variables that impact consumers' intentions to implement voice biometric system in banking system. This process yielded valuable insights. Findings/Result: The ABCD analytical approaches of voice biometric systems in banking indicates the enhanced customer satisfaction and streamline operations through their diverse functionalities. When integrated into the bank's multi-factor authentication framework, voice biometric systems serve as a non-intrusive yet highly secure authentication method. They offer a seamless customer experience by eliminating the need for traditional PINs or passwords, reducing the risk of unauthorized access, and bolstering overall system security. The implementation of voice biometric systems in the banking sector provides several key benefits. Originality/Value: Utilizing the ABCD analysis approach, this research examines the inclination of consumers to activate the voice biometric systems in banking. The investigation delves into consumer behavior and the elements that impact usage decisions, employing determinant issues, key attributes, factor analysis, and elementary analysis. Paper Type: Empirical Analysis

Deep learning enhanced fluid-structure interaction analysis for composite tidal turbine blades

A precise and cost-effective prediction tool for fluid-structure interaction (FSI) analysis is crucial for optimizing the structural design of tidal turbine blades. However, the high computational costs associated with fluid dynamic analysis pose a significant challenge, as the current lack of efficient FSI prediction methods hinders the advancement of cutting-edge tidal turbine designs. To address this issue, this paper proposes a novel consolidated framework that integrates deep learning convolutional neural networks (CNN) with blade element momentum (BEM) theory and finite element method (FEM) to perform deformation analysis of turbine blade structures. The proposed CNN-BEM-FEM integrated framework efficiently identifies the geometric features and predicts the hydrodynamic parameters of turbine blades and thus, achieving accurate assessments of the structural behavior of tidal turbines. The study applies two-step verification procedures to validate the prediction accuracy of the CNN-BEM-FEM framework and the result demonstrates excellent agreement with experimental tests for hydrodynamic performance and blade deformation. When compared with the static one-way FSI calculated by Ansys Workbench software, the computational efficiency of CNN-BEM-FEM framework increases by more than 18 times, with discrepancies in blade deformation and equivalent stress calculations generally less than 5 %. By applying the proposed method to predict the FSI performance of tidal turbine blades with various shear web structures, the practical applicability for composite turbine blade design is successfully demonstrated. The results underscore the potential of the CNN-BEM-FEM framework as an efficient and accurate prediction tool for optimizing the structural design of tidal turbine blades.

UAV trajectory tracking under wind disturbance based on novel antidisturbance sliding mode control

With the widespread application of unmanned aerial vehicles, harsh environments require higher control performance. Numerous existing works offer solutions for handling external disturbances during flight; however, the timeliness of the disturbance compensation is rarely discussed. This study focuses on trajectory tracking under external wind disturbances, specifically under two types of interference: average wind and wind shear. After compensating for the disturbance, a novel antidisturbance sliding mode control is designed based on reference model, to realize accurate, robust, and chatterless trajectory tracking under wind disturbance. Firstly, we addressed the limitations of conventional extended state observers, nonderived forms, and inadequate use of information by introducing state compensation function observers to enhance estimation accuracy for states and disturbances that are challenging to measure directly. Moreover, the disturbance variation is estimated using tracking differentiator and then compensated in the controller to enhance antidisturbance sensitivity. In addition, the effectiveness and stability of the proposed observer and controller are analyzed. Finally, a two-step verification is presented, including simulation with input disturbances and actual flight under two wind field types; multiple industrial fans are used to generate the average wind and wind shear during the experiment. Two robust control methods, state-of-the-art active disturbance rejection control and sliding mode control, are also implemented for comparison, and the results indicate that more than 61.2% accuracy improvement over comparison methods is achieved in simulation and actual experiments.

Does AI signal a new age of cyberthreats?

As new AI technologies make online impersonation easy and facilitate the crafting of faultless and even personalised phishing emails, we need to radically improve our authentication methods. Too many people still rely on outdated methods such as id/password combinations or SMS-based two-factor authentication. The technology already exists to enable us to prove we are who we say we are online. So the time has come to get everyone using it.

Positive or Negative Sentence Feedback Identifier

Abstract: The "Positive or Negative Sentence Feedback Identifier Project" is a natural language processing (NLP) initiative designed to automatically classify and analyse sentences or text- based feedback as either positive or negative. This project aims to streamline sentiment analysis and opinion categorization by employing machine learning and NLP techniques. Its potential applications include sentiment analysis in customer reviews, social media monitoring, and opinion mining, ultimately providing valuable insights to businesses and individuals. It's crucial to gauge the overall sentiment and tone of the feedback. Positive feedback typically conveys satisfaction, using enthusiastic language and specific compliments about at's working well. In contrast, negative feedback often carries a critical tone, highlighting issues or areas of concern with the project. Pay attention to emotional indicators, the specificity of comments, and whether the feedback offers constructive suggestions for improvement. Evaluating feedback within the context of the project's goals and objectives is essential to understanding its impact and guiding necessary actions for project enhancement. Enhance security with two-factor authentication and encryption, while implementing user authentication and authorization controls. Integrate third-party services for expanded functionality and ensure mobile responsiveness for an improved user experience. Explore AI for automation and data analysis, offer robust analytics, and gather user feedback. Prioritise accessibility, optimise performance, and gamify the project with badges and leaderboards. Enable offline functionality, foster community engagement, and align features with userneeds and scalability goals. The project's development encompasses a comprehensive range of solutions, including the implementation of a sophisticated sentiment analysis model, efficient data collection and preprocessing methods, and the incorporation of scalable real-time processingarchitecture. Furthermore, the project's innovative facets encompass a dynamic blend of emotion analysis, contextual analysis, and predictive sentiment analysis, which significantlyaugment its adaptability and effectiveness across diverse domains and use cases.

Securing the Cloud: Understanding Threats and Countermeasures

Abstract: Cloud computing has completely transformed how businesses and individuals store, manage, and retrieve data. Nevertheless, despite its many benefits, there are inherent security challenges. This research investigates the different risks encountered by cloud computing, which consist of data breaches, insider threats, and denial of service attacks. It also covers successful prevention methods like encryption, multi-factor authentication, routine security checks, and staff education. By comprehending these dangers and putting in place strong security measures, organizations can improve the security of their cloud environments and guarantee the confidentiality, integrity, and availability of their data. Cloud computing is now being extensively researched and utilized in both academic and industrial sectors. Both cloud service providers (CSPs) and consumers benefit from cloud computing. The security issues related to cloud computing have been extensively researched in various studies.

Identity Based Authentication Scheme (IAS) for Securing WSN Based Internet of Things

The growing number of connected devices in IoT networks has raised security concerns. These networks face unique challenges, including limited resources, lack of standardized security protocols, and diverse devices and applications. Identity-based authentication is needed to address these threats and limitations. Implementing a multi-factor authentication system can enhance security and protect sensitive information from unauthorized access. The proposed Identity Based Authentication (IAS) scheme is implemented using the NS-2 simulator to address security challenges in IoT networks. Each node is assigned a unique identity for authentication, ensuring only authorized access and preventing security breaches. The IAS scheme enhances IoT network security by verifying node identities. Two scenarios are considered to evaluate the scalability and interoperability of the IAS scheme. The first scenario involves a random environment, assessing performance in real-world settings with varying device densities and network conditions. The second scenario involves a cluster-based environment, providing insights into the scheme's functionality and efficiency. By examining both scenarios, this research aims to provide a comprehensive understanding of how the authentication scheme performs in different deployment scenarios. The findings highlight the importance of selecting the appropriate authentication scheme based on the specific network environment and requirements. The superiority of the IAS authentication scheme suggests its potential for widespread adoption and implementation in various network scenarios over other authentication schemes.

DIGITAL ASSETS MARKET HUB

Block chain technology, non-fungible tokens (NFTs), and cryptocurrency have all contributed to the explosive rise of the digital assets sector. The dynamic and cutting-edge digital asset marketplace shown in this abstract is intended to satisfy the various demands of traders, investors, and enthusiasts in the digital asset market. The marketplace offers a user-friendly and safe platform for purchasing, selling, and managing different digital assets, and it is distinguished by its user-centric approach. It offers real-time market data, order book information, and robust analytical capabilities for well-informed decision-making, all with an intuitive interface that streamlines asset transactions. The ability to move assets seamlessly between blockchain networks, multi factor authentication, cold storage, and complex trading algorithms that optimise trading tactics are some of the key characteristics. ----------------------------------------------------------------------------------------------------------------------------------- Keywords Blockchain, Smart Contract, Ethereum, Transparency, Data sharing, Privacy, Byzantine fault tolerance, decentralized systems.

Multi-factor authentication scheme based on custom attributes

Multi-factor authentication scheme based on custom attributes

Smart and Secure E-Voting Application using Blockchain Technology

The realm of electronic voting systems is experiencing a profound transformation propelled by technological advancements. In response to the demand for secure and transparent voting mechanisms,this project, supervised by Mrs. V.M. Khanapure, endeavors to develop a Smart and Secure E-Voting Application employing blockchain technology. Traditional voting methods often face challenges related to tampering and fraud, which the proposed system aims to mitigate through the decentralized and immutable nature of blockchain. By leveraging the strengths of blockchain, such as transparency and decentralization, the project seeks to establish a trustworthy and efficient electronic voting solution. A primary focus of the Smart and Secure E-Voting Application is the implementation of advanced security measures. Cryptographic techniques are incorporated to secure vote transmissions and protect voter privacy. The application employs end-to-end encryption and multi-factor authentication, fortifyingthe overall security posture of the system. Moreover, the user-friendly interface caters to both voters and election administrators, ensuring accessibility without compromising the high level of security. This project not only addresses current challenges in electronic voting but also lays the foundation for future innovations, setting a precedent for reliable and transparent electoral processes through the utilization of blockchain technology..

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.