Efficient Iris Segmentation for Biometric Authentication in IoT using BAT Algorithm

Resource limitation is quite popular in many Internet of Things (IoT) devices and eavesdropping on the identities of IoT devices could reveal the sensitive information; therefore, high efficiency (computation and communication) and anonymity protection are two desirable properties in IoT authentication and in device-to-device (D2D) authentication. Conventionally, dynamic pseudonyms are widely adopted to protect the device identity privacy in IoT authentication and in D2D communications; however, the conventional mechanisms of pseudonym-renewing and pseudonym-bound-public-keys updating could be very costly or be vulnerable to the desynchronization-based denial-of-service (DoS) attacks. In this paper, we propose a novel 2-level composite hashing (2LCH) mechanism to mitigate the problems, and propose the 2LCH-based anonymous IoT and D2D authentication schemes. The schemes simultaneously achieve high efficiency and strong anonymity for such environments; once two devices successfully complete one instance of the server-assist anonymous authentication, they can run several instances of the direct D2D anonymous authentication without the involvement of the server. The merits of the schemes include: (1) high efficiency in terms of computation and communication; (2) easy and efficient generation/synchronization of dynamic pseudonyms; (3) robustness to both desynchronization-based DoS attacks and the unreliable connections; (4) easy application to the existent IoT architectures and standards; and (5) formal security verification.

The Internet of Things (IoT) is a concept that is transforming our everyday life. Because of its capacity to change people's lives, it has become a vital element of our lives. IoT devices are being used by an increasing number of businesses because they provide new opportunities for wearable devices, home appliances, and healthcare. With all of these possibilities, the risks associated with IoT security are increasing. One of the most challenging aspects of any IoT application is device identification. Things in the IoT share and process data without the need for human interaction. As a result of their total authority, these entities must authenticate and recognize one another effectively. Failure to legitimately authenticating the IoT devices can make them vulnerable to a number of assaults such as DDoS and replay attacks. It is nearly hard to develop an effective authentication system due to the size and other characteristics of IoT. Although a lot of work has been done on this issue, the majority of these solutions rely on a centralized system. These centralized systems are segregated and incompatible with one another, making information exchange between them impossible. In addition, once a centralized authority is attacked, the user's privacy can be exposed easily. Proof of security, decentralization, and anonymity characteristics of blockchain can help address these issues. With blockchain technology applied to IoT systems, obstacles to IoT architecture development and security can be overcome. This paper, therefore, focuses on covering current advancements made in the application of blockchain for authentication in IoT. There is a lack of survey papers on the use of blockchain for authentication in IoT this paper will serve as an aide. This paper will also add to the knowledge of researchers who are interested in IoT security.

Internet of things (IoT) is the integration of computer-based systems and the physical world in which things interact with each other. Due to heterogeneity and resource-constrained feature of IoT devices, there are many privacy and security challenges resulting in many threat vulnerabilities in IoT environments. After reviewing and analyzing the recent IoT security, privacy, and authentication protocols, we will withdraw research gaps focused on the elimination of human factors in IoT authentication. In order to fill these research gaps, this paper proposes a privacy-preserving machine authenticated key agreement based on IoT, denoted as IoTMAKA. IoTMAKA uses dynamic identity and machine fingerprint to provide security and privacy. Security analysis shows that IoTMAKA provides anonymity and untraceability, provides freshness, and is secure against passive and active attacks. IoTMAKA reduces communication overheads by 20% and computational overheads by 25% on average as compared to the previous related works.

Biometric authentication systems offer a secure and convenient method of verifying the identity of individuals based on their unique physiological or behavioral traits. This paper presents a comprehensive survey of biometric authentication systems, focusing on the principles, technologies, applications, challenges, and future directions in the field. The survey covers various biometric modalities, including fingerprint recognition, iris recognition, facial recognition, keystroke dynamics, voice recognition, and gait analysis. Key topics discussed include the use of multi-modal biometric systems, continuous authentication, machine learning, and AI in biometrics, and biometric encryption. The paper also explores the implementation of biometric authentication in access control systems, financial transactions, healthcare systems, and government and law enforcement. Challenges such as security and privacy concerns, spoofing and impersonation, scalability and integration, and ethical and legal issues are addressed. Future research opportunities include improving accuracy and reliability, enhancing security and privacy, addressing societal and ethical concerns, and integrating biometric authentication with the Internet of Things (IoT).

Internet of things (IoT) is considered as a collection of heterogeneous devices, such as sensors, Radio-frequency identification (RFID) and actuators, which form a huge network, enabling non-internet components in the network to produce a better world of services, like smart home, smart city, smart transportation, and smart industries. On the other hand, security and privacy are the most important aspects of the IoT network, which includes authentication, authorization, data protection, network security, and access control. Additionally, traditional network security cannot be directly used in IoT networks due to its limitations on computational capabilities and storage capacities. Furthermore, authentication is the mainstay of the IoT network, as all components undergo an authentication process before establishing communication. Therefore, securing authentication is essential. In this paper, we have focused on IoT security particularly on their authentication mechanisms. Consequently, we highlighted enormous attacks and technical methods on the IoT authentication mechanism. Additionally, we discussed existing security verification techniques and evaluation schemes of IoT authentication. Furthermore, analysis against current existing protocols have been discussed in all parts and provided some recommendation. Finally, the aim of our study is to help the future researcher by providing security issues, open challenges and future scopes in IoT authentication.

Research on the Internet of Things (IoT) has been a hot spot in recent years. Resource authentication and management in IoT is a most important and challenging problem and is far to be solved. In this paper, we propose an effective distributed hierarchical IoT resource authentication and management model based on the key technologies of distributed hash table, Chord Ring and public key infrastructure. By using the exceptional advantage of search and Research on the Internet of Things (IoT) has been a hot spot in recent years. Resource authentication and management in IoT is a most important and challenging problem and is far to be solved. In this paper, we propose an effective distributed hierarchical IoT resource authentication and management model based on the key technologies of distributed hash table, Chord Ring and public key infrastructure. By using the exceptional advantage of search and query ability of the above technologies, the proposed model owns enhanced security and improved efficiency. Simulation results show that the proposed model can mutually authenticate the IoT end entities each other in more short time compared to other existing approaches. query ability of the above technologies, the proposed model owns enhanced security and improved efficiency. Simulation results show that the proposed model can mutually authenticate the IoT end entities each other in more short time compared to other existing approaches.

The biometric traits of any human being such as fingerprint, tongue prints, face recognition, Retina Scan, Iris recognition etc. are unique and cannot be replicated or fabricated using modern technologically advanced processes. These unique traits are used to identify individuals for grating access to sensitive data over the cloud. In this paper we propose a biometric authentication system based on Iris recognition to allow user identity based data to be pushed on to the cloud for global access, if the user Iris image is authenticated with the database. The real time Iris scanner captures the Iris image and same is imported to the MATLAB workspace for feature extraction and matching. Upon successful matching, the data is transmitted to the ThingSpeak Internet of Things (IoT) analytics platform. The IoT framework is a massive network of physical devices sharing data according to set protocols. Most of the IoT enabled devices lack security protocols. Thus, biometric authentication in general and iris recognition in particular finds a perfect fit for securing data over an IoT framework.

With the rapid growth of Internet of Things (IoT), it can be foreseen that IoT services will be influencing several use-cases. IoT brings along the security and privacy issues that may hinder its wide scale adoption. The scenarios in IoT applications are quite dynamic as compared to the traditional internet. It is vital that only authenticated and authorised users get access to the services provided. Hence, there is a need for a novel authentication and access control technique that is compatible and practically applicable in diverse IoT scenarios to provide adequate security to devices and data communicated. This article aims to highlight the potential of blockchain for enhanced and secured authentication and access control in IoT. The proposed method relies on blockchain technology, which tends to eliminate the limitations of intermediaries for authentication and access control. As compared to existing systems, it has advantages of decentralisation, secured authentication, authorisation, adaptability, and scalability.

The number of network and sensor-enabled devices in the Internet of Things (IoT) domains is growing extremely, leading to a huge production of data. These data contain important information which can be used in various areas, such as science, industry, medical, and even social life. To make the IoT system smart, the only solution is entering the world of machine learning. Many machine learning algorithms are introduced for handling such a huge amount of IoT data. It is very difficult to find the best-suited algorithm for problems in the IoT domain. This study combined three ensemble models and proposed a new model termed the “hybrid model”. A set of features are extracted from the raw IoT datasets from diverse IoT domains, using Principal component analysis (PCA), Linear discriminant analysis (LDA), and Isomap for classification problems. Performance comparison of the classifiers is provided in terms of their accuracy, area under the curve (AUC), and F1 score. This comparative study’s experimental result shows that Hybrid with PCA and Stacking ensemble technique in particular with PCA have better overall performance than other ensemble techniques for binary class and multie class datasets respectively

With its advantages are gradually emerging, the Internet of Things (IoT) is profoundly changing the way people work and live. Among all IoT applications, medical IoT is partic-ularly important, in which the user can communicate with smart medical device through the hospital gateway node. However, due to the inherent defects of these smart devices and the openness of wireless networks, medical IoT is vulnerable to kinds of attacks, such as impersonation attack and password guessing attack. Unfortunately, there are few authentication schemes for medical IoT at present, while existing three-party schemes have various weakness and are not suitable for medical IoT. Given the sensitivity of patient data and the deadly consequences of attacks on medical devices, there is an urgent need to develop a suitable authentication scheme in Medical IoT Network with high security. To alleviate the above problems, a robust ECC based three-party authentication and key agreement scheme for medical IoT(RAKI) has been proposed, which is secure with random oracle model and the informal security analysis. Besides, the performance comparisons against existing competing three-party schemes indicate that our scheme is efficient for medical IoT.

Designing an effective system for women's safety is essential for reducing crimes against women and prosecuting the guilty. Women harassment at workplace, public transport etc., has been a rising concern all over the world. An early action and proper mechanism could reduce the number of crimes against women. The development of a Women Safety Smart System within the Internet of Things (IoT) domain represents a significant advancement in leveraging technology to address the pressing issue of women's safety. This paper outlines the design, implementation, and potential impact of such a system, which integrates IoT devices, sensors, and data analytics to create a proactive safety infrastructure. Key components of the system include IoT-enabled wearables, smart surveillance and monitoring systems, a dedicated mobile application, and predictive analytics capabilities. These components work synergistically to provide real-time monitoring, timely intervention, and community engagement in ensuring the safety and well-being of women. By harnessing IoT technology, this system offers a scalable and adaptable solution to mitigate risks, empower women, and foster societal change. The paper discusses the benefits, challenges, and future directions of Women Safety Smart Systems in the IoT domain, highlighting their potential to transform safety paradigms and promote inclusivity and equality in urban environments.

Biometrics based individual identification is observed as an effective technique for automatically knowing, with a high confidence a person's identity. Multi-modal biometric systems consolidate the evidence accessible by multiple biometric sources and normally better recognition performance associate to system based on a single biometric modality.Multi biometric systems are used to overcome this issue by providing multiple pieces of indication of the same identity. This system provides effective fusion structure that combines information provided by the multiple field experts based on decision-level and score-level fusion method, thereby increasing the efficiency which is not conceivable in uni-modal system.Multi-modal biometrics can be attained through a fusion of two or more images, where the subsequent fused image will be more protected. This paper discusses various fusion techniques, architecture of multi-modal biometric authentication and working of biometric fusion i.e. Iris and Fingerprint recognition that are used in multi-modal biometrics.

IoT devices rely on authentication mechanisms to render secure message exchange. During data transmission, scalability, data integrity, and processing time have been considered challenging aspects for a system constituted by IoT devices. The application of physical unclonable functions (PUFs) ensures secure data transmission among the internet of things (IoT) devices in a simplified network with an efficient time-stamped agreement. This paper proposes a secure, lightweight, cost-efficient reinforcement machine learning framework (SLCR-MLF) to achieve decentralization and security, thus enabling scalability, data integrity, and optimized processing time in IoT devices. PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication. An IoT network gathers information of interest from multiple cluster members selected by the proposed framework. In addition, the software-defined secured (SDS) technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform. Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime, energy, secured data retrieval rate, and performance ratio. By enabling the proposed framework, number of residual nodes is reduced to 16%, energy consumption is reduced by up to 50%, almost 30% improvement in data retrieval rate, and network lifetime is improved by up to 1000 msec.

The Internet of Things (IoT) has many applications, including Industry 4.0. There are a number of challenges when deploying IoT devices in the Industry 4.0 setting, partly due to the low-cost IoT devices/nodes with limited capacity to run/support security solutions. Hence, there is a need for a lightweight and efficient security solution to protect the environment. Thus, in this article, we present a robust, lightweight, and provably secure authentication and key agreement protocol specifically for the IoT environment based on a hierarchical approach. The proposed protocol relies on lightweight operations, such as elliptic curve cryptography, physically unclonable functions, hash functions, concatenation, and XOR operations. We then evaluate the security of the designed protocol, including the widely used automated validation of Internet security protocols and applications (AVISPA), and demonstrate that it supports mutual authentication between IoT nodes and server, and is resilient against a number of common security attacks [denial of service (DoS), replay, spoofing, etc.]. The computational and communication overhead analysis shows that the proposed protocol is comparatively less expensive than three other recently published, competing protocols.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.