Research on Innovative Secure Authentication Methods for Seamless Roaming in WAPI

The opportunity to access on-demand, unbounded computation and storage resources has increasingly motivated users to move their health records from local data centers to the cloud environment. This change can reduce the costs associated with the management of data sharing, communication overhead, and improving Quality of Service (QoS). Processing, storing, hosting, and archiving data related to E-Healthcare systems without physical access and control can exacerbate authentication and access control issues in this new environment. E-Healthcare systems require authentication services that are strong enough to ensure data confidentiality and privacy, and flexible to meet the needs of health professionals and patients. Therefore, convincing users to move sensitive medical records to the cloud environment requires implementing secure and strong authentication and access control methods to protect the data. This chapter proposes a new authentication approach that preserves both authentication and access control in cloud-based e-Health systems. In each access request, based on the maximum rights of the user, the minimum access is extracted. The focus of this work is the authentication process. We propose an authentication approach for e-Healthcare systems based on cloud computing. Its main characteristics are: flexibility of configuration for the authentication mechanisms and the use of a robust system for recording events. We analyze our scheme with respect to data confidentiality and resistance to common attacks on the network. Experimental results show that the proposed method tolerates a high number of concurrent authentication requests with a reasonable response time. In this chapter we deal with the engineering requirements of the security system and the details of its implementation. We also discuss the efficacy and ease of use of different authentication methods.

User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. For several decades, user authentication was based on the “something the user knows”, known also as knowledge-based user authentication. Recent studies state that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the “something the user is” has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. In this context, we, firstly, present an overview of user authentication on mobile devices and discuss risk-based user authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Afterwards, a set of novelty detection algorithms for risk estimation is tested and evaluated to identify the most appropriate ones for risk-based user authentication on mobile devices.

In this paper, we propose a new authentication method to prevent authentication vulnerability of Claim Token method of Membership Service provide in Private BlockChain. We chose Hyperledger Fabric v1.0 using JWT authentication method of membership service. TOTP, which generate OTP tokens and user authentication codes that generate additional time-based password on existing authentication servers, has been applied to enforce security and two-factor authentication method to provide more secure services.

Computing device authentication, the process of proving one's identity to a computing device (e.g., laptop, tablet, smart phone, etc.), is critical to protecting the sensitive data these devices contain. However, computing device authentication often makes assumptions about the user's ability to perform complex tasks with their arms, hands, and fingers. For example, entering complex passwords, or accurately positioning a camera for facial recognition. This can create barriers for people with upper extremity impairment (UEI) who have reduced strength, accuracy, dexterity, speed, and/or range of motion in their shoulders, upper arms, forearms, hands, and/or fingers. My goal is to design new, more accessible computing device authentication for people with UEI. I detail my approach to address this goal in four core steps. I first explore the current state of computing device authentication for people with UEI by conducting 8 semi-structured interviews with people with UEI. I then follow up to determine what impacts the COVID-19 pandemic had on the computing device use of people with UEI by conducting 6 semi-structured interviews with people with UEI. Using the results from these interviews, I propose two novel forms of authentication designed for people with UEI: (1) ballistocardiogram-based authentication, and (2) ALPACA, a recognition-based graphical authentication method. I evaluate both of these methods and show their potential viability as secure and accessible authentication methods for people with UEI. Based on my findings throughout these four manuscripts, I suggest seven areas of future research.

Authentication mechanisms are considered the typical method to secure financial websites. Context authentication has become increasingly important in the arena of online banking, which involves sensitive data that belong to users who trust their banks. Multifactor authentication is the most commonly used method of strengthening the log-in process in e-banking. Developing a usable and secure authentication approach and method is the most challenging area for researchers in the fields of security and Human-Computer Interaction (HCI). This paper describes a work-in-progress towards a new approach for authenticating users when access online banking by giving them the opportunity to choose their preferred method to log into e-banking. In our complex experiment with 100 online banking customers, we simulate an original online banking platform based on the proposed approach; then, we evaluate the usability and security of three different methods and assess user awareness of the most visible security design flaws. The initial result shows that the new system model was able to assess the usability and security of different multifactor authentication methods and it is considered a first attempt towards a usable and secure authentication approach.

The security of streaming data should be ensured in current complex data era in order to provide a trusted and secure network environment. To authenticate the scalable video coding (SVC) streams by fully utilizing its decoding relationship without reducing its scalability, we establish an acyclic and directed decoding dependence graph (DDG) on the logical units of SVC steams. By applying the topological sort on DDG, we obtain the hash appendence mode for different layers of the streams (i.e., spatial and temporal layers). We propose a secure and efficient SVC authentication method based on the deduced hash appendence mode. With regard to the quality layer, we consider the corresponding quality data packets with unequal importance, and propose a grouping authentication strategy with constrained group lengths. We form the optimization problem of minimizing the authentication cost, and solve it by an iteration method. Simulation results show that our authentication approach can achieve much less computation cost and much lower overhead, while it can preserve higher verification rates and better recovered quality of video as compared with other state-of-the-art methods.

The healthcare ecosystem is migrating from legacy systems to the Internet of Things (IoT), resulting in a digital environment. This transformation has increased importance on demanding both secure and usable user authentication methods. Recently, a post-quantum fuzzy commitment scheme (PQFC) has been constructed as a reliable and efficient method of biometric template protection. This paper presents a new two-factor-based user authentication protocol for the IoT-enabled healthcare ecosystem in post-quantum computing environments using the PQFC scheme. The proposed protocol is proved to be secure using random oracle model. Furthermore, the functionality and security of the proposed protocol are analyzed, showing that memoryless-effortless, user anonymity, mutual authentication, and resistance to biometric templates tampering and stolen attacks, stolen smart card attack, privileged interior attack are fulfilled. The costs of storage requirement, computation, communication and storage are estimated. The results demonstrate that the proposed protocol is more efficient than Mukherjee et al., Chaudhary et al., and Gupta et al. protocols.

With the rapid growth of digital technologies, the demand for reliable and secure authentication methods has become increasingly vital. Among various biometric identification techniques, iris recognition stands out as one of the most dependable approaches due to the iris’s unique and stable pattern that remains consistent throughout a person’s life. Unlike traditional password- or token-based systems, iris recognition offers a higher level of accuracy, security, and resistance to environmental and age-related variations. This paper presents a comprehensive study of the Iris Recognition System (IRS), encompassing both classical and modern methodologies. The research highlights the traditional techniques such as Daugman’s rubber sheet model and Gabor wavelet-based feature extraction, while addressing their limitations in uncontrolled or noisy conditions. To overcome these challenges, the proposed approach integrates advanced deep learning methods like Convolutional Neural Networks (CNNs) and transfer learning models such as DenseNet201, which enhance feature extraction and classification accuracy. The workflow includes essential stages such as image acquisition, preprocessing, iris localization, normalization, feature extraction, and pattern matching. The proposed system was evaluated on standard datasets including CASIA, UBIRIS.v2, and IITD, achieving a recognition accuracy of over 95% even under challenging conditions like poor lighting and off-angle captures. The paper also explores key applications of iris recognition in domains such as border control, mobile authentication, e-governance, and healthcare. Finally, it discusses potential advancements like lightweight CNN architectures, multimodal biometric systems, and edge-based iris recognition models for future research and deployment.

The use of MapReduce (MR) model has attracted much attention because of the reductions in the costs involved in satisfying the storage and computational demands. However, authentication methods proposed for such model is subjected with rooms for improvements. In the initial design and implementation of the MR model such as Hadoop, issues of security including authentication was not considered. It was assumed that the underlying system worked in a trustworthy environment, so MR developers did not provide authentication methods in the MR model. This assumption is still valid to some extent, whereas major IT players in the world, such as Facebook and Yahoo have implemented the MR model in their private clouds. However, recently there are efforts to provide the MR model in public clouds. Inadequate authentication methods for the MR model in such environment makes the job execution work-flow vulnerable to many attacks such as impersonation and spoofing attacks. So as part of our work to design a secure and effective authentication solution for the MR model, this paper examines and critically analyses the state-of-the-art in MR authentication methods against a general set of specified requirements. It is to identify limitations, weaknesses, and missing features in the proposed authentication methods for the MR model.

Chapter 11 - Badge Surveillance

Integrity of image data plays an important role in data communication. Image data contain confidential information so it is very important to protect data from intruder. When data is transmitted through the network, there may be possibility that data may be get lost or damaged. Existing system does not provide all functionality for securing image during transmission. i.e image compression, encryption and user authentication. In this paper hybrid cryptosystem is proposed in which biometric fingerprint is used for key generation which is further useful for encryption purpose. Secret fragment visible mosaic image method is used for secure transmission of image. For reducing the size of image lossless compression technique is used which leads to the fast transmission of image data through transmission channel. The biometric fingerprint is useful for authentication purpose. Biometric method is more secure method of authentication because it requires physical presence of human being and it is untraceable.

Fintech services have been massively implemented in many countries, especially Indonesia, but in some cases, security aspects in fintech have been monitored due to data breaches and security standards in protecting user data. On the market survey of 350 respondents, 95% of respondents actively using payment fintech, and 24.6% lending fintech. In maintaining security and usability, 63,9% of respondents are willing to spend time on data security and 50,9% are willing to upgrade their device to meet a certain level of security. On the technical simulation of biometric authentication, the average fingerprint accuracy rate is 91.64% while facial recognition is 91.82%. Various authentication methods meet a minimum of 90% accuracy as NIST and Google Android standards. Fingerprint processing time and facial recognition processing time are approx. 18.24 msec and 31.83 msec, while the combination with other password methods only takes <50 msec. Based on the market survey and technical simulations show that biometrics can be applied to secure user data and in combination with other authentication methods provide better security and usability of user authentication in fintech.

A password-based user-authentication method is inputted from the keyboard, but the safety of the data is no longer ensured due to the theft of keyboard data. For this reason, a novel authentication method emerged whereby both mouse data and screen information are used, and this image-based authentication does not require the keyboard for the input of the password. This important method authenticates users based on the clicked information from the mouse that are within the specific positions of a displayed image. Just like the password-authentication method for which the keyboard is used, the security of the image-based authentication method is also now threatened because the displayed image and the clicked information of the mouse can be exposed; furthermore, the research studies that have assessed these security threats are insufficient. In this paper, the security of the image-based authentication is therefore analyzed according to the screen-capture tools that are easy to obtain from the Internet. The results of this paper can be utilized as a reference to improve the security of the image-based authentication in the future.

T Fake drugs threatens medical supply chain frightfully over 124 countries in the world. Even in Japan, although nobody expected counterfeits violate the maintained distribution of this country, fake harvoni for hepatitis C sold at an authorized pharmacy shocked officials in Ministry of Health, Labor and Welfare. Anti-counterfeit to prevent fake drugs requires rapid-authenticating, costless, and more than anythings, high-security technology. Recent advances in nanotechnology accelerate developments of nanotag identifiers. Thinking of rapidness and high security in authentication, surface enhanced Raman scattering (SERS) produced from well-designed noble metal nanostructure is promised to apply new authentication method. We have developed SERS active nanostructures by self-assembling of gold nanoparticles (AuNP) with Raman active reporting molecules. A few nanogram amounts of the AuNP assembles were directly deposited onto commercial tablet Mucodyne. For on-dose authentication outside of a laboratory, 80 g weight and smart-phone driven palm-top Raman detector was used. Shelf-life in SERS activity of the AuNP self-assemble liquid stocked at room temperature was over six months. The nanotag labeled Mucodyne tablet showed special SERS signal derived from the reporting molecule, and 0.2 s irradiation of 785 nm (5 mW) laser was enough to produce distinguishable SERS spectra from intact tablet. Excellent longevity of SERS signal over three years on the labeled tablets was confirmed. By just 0.2 second laser irradiation with a palmtop Raman spectrometer, rapid on-dose authentication was successfully achieved.

Personal Identification Numbers (PINs) are widely used today for user authentication on mobile devices. However, this authentication method can be subject to several attacks such as phishing, smudge, and side-channel. In this paper, we increase the security of PIN-based authentication by considering behavioral biometrics, specifically the smartphone movements typical of each user. To this end, we propose a method based on anomaly detection that is capable of recognizing whether the PIN is inserted by the smartphone owner or by an attacker. This decision is taken according to the smartphone movements, which are recorded during the PIN insertion through the built-in motion sensors. For each digit in the PIN, an anomaly score is computed using Machine Learning (ML) techniques. Subsequently, these scores are combined to obtain the final decision metric. Numerical results show that our authentication method can achieve an Equal Error Rate (EER) as low as 5% in the case of 4-digit PINs, and 4% in the case of 6-digit PINs. Considering a reduced training set, composed of solely 50 samples, the EER only slightly worsens, reaching 6%. The practicality of our approach is further confirmed by the low processing time required, on the order of fractions of milliseconds.