Secure Access Control Research Articles

IntelliDoor: Smart Access Control System with Automated Door Operation

Abstract: The Intelligent Access Control System with Automatic Door Operation is a project aimed at providing secure and efficient access control using artificial intelligence (AI) and automated door mechanisms. This system utilizes facial recognition technology for user identification, Firebase for data storage and management, and Arduino for controlling the automatic door operation. The objective is to develop a robust and intelligent access control system that automatically identifies authorized individuals through facial recognition and opens the door for them. The system also maintains a record of entry for each individual for attendance tracking or security purposes. The project features include facial recognition, automatic door operation, Firebase integration, an alert system, and real-time monitoring. The project workflow involves initialization, facial recognition, recognition results, database updates, and automatic door operation. The project report covers the introduction, literature review, methodology, implementation, results, and conclusion. The Intelligent Access Control System with Automatic Door Operation provides an efficient and secure solution for access control, enhancing security while offering convenience. With further refinement and integration, it has the potential to become a standard solution for access control in modern buildings and facilities.

[formula omitted]: A blockchain-based secure access control management for the Internet of Things

The Internet of Things (IoT) paradigm has widespread applications across many fields in which private and sensitive user or environmental data are sensed and shared. Most present-day IoT applications depend on centralized cloud servers for authentication and access control. Validating the identity of a user and determining the legitimacy of his/her access requests require multiple rounds of data communications over the untrusted Internet, exposing sensitive data to potential attacks. Thus, protecting these data from security and privacy attacks and ensuring legitimate access is imperative. To address this challenge, we adopt an emerging technology called blockchain to propose a decentralized security framework called BloAC. It ensures secure access control in IoT networks without the intervention of the back-end cloud. We have used the Hyperledger Fabric, an open-source, permissioned blockchain platform, for implementing a prototype system using customized attribute-based access control (ABAC) policies. We have performed simulated and real test bed-based experiments to illustrate that BloAC outperforms the cloud–server-based access control in latency and scalability, significantly reducing latency by up to 42.45% compared to cloud-based solutions. Finally, we conduct a security analysis to formally verify the ABAC policies used in BloAC and establish its robustness against attacks theoretically and using the AVISPA tool.

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.

Vehicle And Driver Recognition for Access Control

The following paper proposes an advanced vehicle and driver recognition system to enhance security at vehicular entry-exit points in response to the growing needs of more efficient and secure access control systems at these checkpoints. The proposed system will integrate license plate recognition and facial recognition technologies using state of-the-art machine-learning models from Ultralytics: YOLOv8 and EasyOCR, respectively. It works in three stages: license plate detection, character alpha-numeric identification, and then driver identity with facial recognition. Hence, the two-tier authentication process based on license plate detection and facial identification ensures further prevention from unauthorized entry. Preliminary evaluation of the system also produced very high values for precision, recall, and the mean average precision, indicating that the VDR system offers substantial advances in effectiveness and security when compared to conventional methodologies for access control.

Blockchain-enhanced efficient and anonymous certificateless signature scheme and its application

Although the Internet of Things (IoT) brings efficiency and convenience to various aspects of people’s lives, security and privacy concerns persist as significant challenges. Certificateless Signatures eliminate digital certificate management and key escrow issues and can be well embedded in resource-constrained IoT devices for secure access control. Recently, Ma et al. designed an efficient and pair-free certificateless signature (CLS) scheme for IoT deployment. Unfortunately, We demonstrate that the scheme proposed by Ma et al. is susceptible to signature forgery attacks by Type-II adversaries. That is, a malicious-and-passive key generation center (KGC) can forge a legitimate signature for any message by modifying the system parameters without the user’s secret value. Therefore, their identity authentication scheme designed based on vehicular ad-hoc networks also cannot guarantee the claimed security. To address the security vulnerabilities, we designed a blockchain-enhanced and anonymous CLS scheme and proved its security under the Elliptic curve discrete logarithm (ECDL) hardness assumption. Compared to similar schemes, our enhanced scheme offers notable advantages in computational efficiency and communication overhead, as well as stronger security. In addition, a mutual authentication scheme that satisfies the cross-domain scenario is proposed to facilitate efficient mutual authentication and negotiated session key generation between smart devices and edge servers in different edge networks. Performance evaluation shows that our protocol achieves an effective trade-off between security and compute performance, with better applicability in IoT scenarios.

Design of Robotics Implementation of A Fingerprint Biometric System

Biometrics, rooted in the Greek terms "bios" (life) and "metrikos" (measure), represents a modern approach to identifying individuals based on their unique physical characteristics, such as fingerprints, facial features, and iris patterns. In today's increasingly digital landscape, traditional security methods like passwords and keys have become inadequate in safeguarding sensitive information. The rise of sophisticated cyberattacks and unauthorized access has highlighted the need for more advanced and secure authentication methods. This project aims to design and implement a fingerprint biometric system using robotics and FPGA technology. The system is intended to provide a more reliable and secure method of verifying identities by capturing and processing fingerprint images. It will extract minutiae points, the unique features within a fingerprint, to create a robust authentication mechanism. This approach promises to enhance security by offering an alternative to conventional methods that are often vulnerable to breaches. In developing this biometric system, the focus will be on ensuring that it can be effectively integrated into various applications where secure access control is crucial. By leveraging the unique and immutable nature of fingerprints, the system will reduce the risks associated with password reuse, theft, and unauthorized sharing. The inclusion of continuous authentication techniques will further strengthen security, ensuring that the user’s identity is verified throughout a session, not just at the initial login. Additionally, the project will incorporate version control mechanisms to track the development and deployment of the system, ensuring that any changes or updates can be easily managed and reviewed. This will help maintain the system's reliability and effectiveness over time, allowing it to adapt to evolving security challenges. Through this work, we aim to contribute to the broader adoption of biometric technologies in securing digital and physical environments, paving the way for more secure and seamless user experiences.

Device-free Vehicle Type Identification for Next Generations Intelligent Transportation System

With the substantial progress in the automotive industry, intelligent vehicle identification systems have become imperative tools for next generation transportation system planning. The advent of internet of vehicles and intelligent cellular vehicular networks has entered the transportation system into a new era of technology. In smart transportation system of beyond 5G/6G vehicular networks, vehicle type identification is of vital importance for ensuring safe operability of autonomous driving environ ments, electronic toll, and traffic management. It can be used in high efficiency roadway services, vehicle theft prevention, road condition monitoring, automatic driving, access control security, etc. During the recent years, channel state information (CS I) of WiFi signal has gained escalating traction for activity or gesture recognition, and person identification with improved robustness and classification accuracy. Therefore, in this research work, a WiFi based vehicle type identification system is proposed for beyond 5G/6G vehicular networks. In this novel approach, we start from vehicle detection using the variation in CSI of WiFisignals caused by the moving vehicle. For vehicle detection we simply apply a threshold based algorithm and then extract meaningful features. Afterwards, the vehicle type is identified using machine learning algorithms. This is a wireless model that enables commercial WiFi devices to identify different types of vehicles using CSI measurements. The presented framework can identify six different types of vehicles with an accuracy of 91.8%

E-SAFE: A secure and efficient access control scheme with attribute convergence and user revocation in fog enhanced IoT for E-Health

The growth of IoT led to a surge in connected devices and data production in the medical field. Therefore, to meet the rising demand for modern healthcare services, Fog and Cloud services come as a rescue for IoT-based equipment. As data travels through several levels, providing security to such data is challenging. The CP-ABE cryptographic approach allows for efficient access control. However, none of the known cryptographic CP-ABE approaches that provide granular access control offers the following features: attribute convergence, privileged access, user revocation, and outsourcing capabilities altogether. Thus, we present e-SAFE, a CP-ABE approach which addresses all these issues. In addition, in e-SAFE, the data users with resource-constrained medical gadgets must save just a constant and small-size decryption key on their gadgets. According to our assessment of security and performance, e-SAFE is found to be a secure and efficient access control technique for IoT gadgets.

SAFEGUARDING CHILDREN’S DATA IN THE DIGITAL AGE: A LEGAL APPROACH TO PREVENT CYBERATTACKS AND CHILD SEXUAL ABUSE

The growth of digital technologies and online platforms has dramatically increased the risk of children becoming victims of sexual abuse. Cybercriminals often abuse children's personal information, such as their name, age, location, and online activities, to identifyand exploit them. However, this digital revolution has also led to increased risks, including the vulnerability of children's data to cybersecurity attacks. One of the most concerning consequences of such attacks is facilitating child sexual abuse. Therefore,protecting children's data is essential in preventing child sexual abuse. In this background based on this hypothesis, this paper focuses on the legal approach to protect the vulnerability of children's data from cybersecurity attacks. The paper analysis the legal frameworks and guidelines developed by the Indian government to safeguard the personal data of children. Further, it would identify the current gaps in the legal framework and emphasise the need for further improvements towards strengthening the data protection of children. Moreover, the paper explores the role of technology in protecting children's data, including data encryption, secure data storage, access controls, and monitoring systems and also analyses the challenges associated with implementing these technological solutions. Finally, the paper offers suggestions for policymakers, legal practitioners, and technology professionals to protect children's data from cyberattacks, thereby preventing child sexual abuse.

A revocable multi-authority attribute-based encryption scheme for fog-enabled IoT

As more applications move data storage to the cloud, protecting sensitive data becomes increasingly important, especially for the Internet of Things (IoT) environments. Ciphertext-policy attribute-based encryption (CP-ABE) is a practical approach for confidentiality and secure access control for data outsourced to the cloud. However, the underlying CP-ABE operations based on bilinear pairings are too demanding for resource-constrained IoT devices. Furthermore, applications such as Industrial IoT (IIoT) have requirements (efficiency, operative, and security) that existing CP-ABE proposals cannot fulfill, and advanced IoT architectures (e.g., fog computing) have not been well-exploited. This paper proposes a novel CP-ABE scheme suitable for IoT scenarios, using an IIoT generic model as a reference. It targets multiple attribute authorities, outsourced encryption and decryption to fog nodes, user revocation, and asymmetric pairings constructions to achieve recommended security levels. As the main distinctive, revocation is defined using a broadcast encryption-based approach, allowing data owners to enforce user revocation over their outsourced data. According to the performance analysis, the proposed scheme achieves high efficiency for IoT nodes. It is also competitive in terms of storage, bandwidth, and computation efficiency compared to previous proposals. Moreover, the security of the suggested construction is demonstrated against chosen-plaintext attacks.

Performance evaluation of efficient segmentation and classification based iris recognition using sheaf attention network

Iris recognition, a precise biometric identification technique, relies on the distinct epigenetic patterns within the iris. Existing methods often face challenges related to segmentation accuracy and classification efficiency. To improve the accuracy and efficiency of iris recognition systems, this research proposes an innovative approach for iris recognition, focusing on efficient segmentation and classification using Convolutional neural networks with Sheaf Attention Networks (CSAN). Main objective is to develop an integrated framework that optimizes iris segmentation and classification. Subsequently, dense extreme inception multipath guided up sampling network is employed for accurate segmentation. Finally, classifiers including convolutional neural network with sheaf attention networks are evaluated. The findings indicate that the proposed method achieves superior iris recognition accuracy and robustness, making it suitable for applications such as secure authentication and access control. By comparing with existing approaches CSAN obtains 99.98%, 99.35%, 99.45% and 99.65% accuracy for the four different proposed datasets respectively.

Security and Privacy Challenges in Software as a Service (SaaS)

This document In the rapidly evolving landscape of cloud computing, Software as a Service (SaaS) has emerged as a dominant model for delivering software applications over the internet. However, this model introduces significant security and privacy challenges. This research paper delves into these issues, highlighting the unique risks associated with SaaS platforms. The study explores common vulnerabilities such as data breaches, unauthorized access, data loss, and identity theft, which are exacerbated by the centralized nature of SaaS solutions where data is stored on remote servers managed by service providers. To address these challenges, the paper underscores the importance of robust security measures, including encryption, access controls, and secure software development practices. Additionally, it emphasizes the necessity for SaaS providers to adopt advanced privacy protection techniques like data anonymization and differential privacy to meet user expectations and comply with stringent legal regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Through a comprehensive review of literature and analysis of current SaaS security practices, the research identifies critical risks and provides strategic recommendations for enhancing security and privacy in SaaS environments. By integrating multiple layers of security throughout the software development lifecycle, enforcing strict access management protocols, and ensuring compliance with regulatory standards, SaaS providers can significantly mitigate risks and safeguard sensitive user data. The findings of this study highlight the imperative for continuous improvement in SaaS security strategies to keep pace with emerging threats and technological advancements. Ultimately, by prioritizing security and privacy, SaaS providers can not only protect their users but also gain a competitive edge in the increasingly crowded market of cloud services.

Review of Security and Privacy-Based IoT Smart Home Access Control Devices

Review of Security and Privacy-Based IoT Smart Home Access Control Devices

Video security in logistics monitoring systems: a blockchain based secure storage and access control scheme

Video security in logistics monitoring systems: a blockchain based secure storage and access control scheme

Auditable and dynamic access control scheme with behavior and identity tracing

Ciphertext policy attribute-based encryption (CP-ABE) is a potential solution to security sharing issues since it offers one-to-many encryption and fine-grained access control. To prevent users from disclosing their access permissions, traceable CP-ABE schemes are emerging. However, the current traceable CP-ABE schemes still have some security problems, such as impromptu attribute retracting, unreliable tracking, and easy disclosure of access policy privacy, making it essential to implement dynamic data security sharing and malicious user auditing. To address these issues, we propose an auditable and dynamic access control scheme with behavior and identity tracing. The scheme first uses an access tree with hidden leaf nodes to express the access policy, improving the flexibility and security of access control. Secondly, the Blockchain and InterPlanetary File System (IPFS) frameworks perform the audit authentication and store the ciphertext, policy, and public keys to avoid tampering with shared data. Furthermore, to prevent users from leaking their decryption keys, a tracing algorithm based on white-box traceability is developed. On this basis, an authority update algorithm is proposed to revoke the malicious user’s permission immediately and update the decrypted key for unrevoked users. An auditable smart contract based on blockchain is proposed to prevent user denial. Finally, theoretical analyses prove that the proposed scheme not only achieves traceability, revocation, authority updating, policy hiding, and audit, but also guarantees security under a chosen plaintext attack model. Experiments demonstrate that our proposal reduces the key generation time by 22.6%, the encryption and decryption time by 39.2% and 59.8%, and the storage costs of ciphertext and private key are reduced by 12.1% and 5.6% over existing schemes.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

Multi-View Knowledge Ensemble With Frequency Consistency for Cross-Domain Face Translation.

Cross-domain face translation aims to transfer face images from one domain to another. It can be widely used in practical applications, such as photos/sketches in law enforcement, photos/drawings in digital entertainment, and near-infrared (NIR)/visible (VIS) images in security access control. Restricted by limited cross-domain face image pairs, the existing methods usually yield structural deformation or identity ambiguity, which leads to poor perceptual appearance. To address this challenge, we propose a multi-view knowledge (structural knowledge and identity knowledge) ensemble framework with frequency consistency (MvKE-FC) for cross-domain face translation. Due to the structural consistency of facial components, the multi-view knowledge learned from large-scale data can be appropriately transferred to limited cross-domain image pairs and significantly improve the generative performance. To better fuse multi-view knowledge, we further design an attention-based knowledge aggregation module that integrates useful information, and we also develop a frequency-consistent (FC) loss that constrains the generated images in the frequency domain. The designed FC loss consists of a multidirection Prewitt (mPrewitt) loss for high-frequency consistency and a Gaussian blur loss for low-frequency consistency. Furthermore, our FC loss can be flexibly applied to other generative models to enhance their overall performance. Extensive experiments on multiple cross-domain face datasets demonstrate the superiority of our method over state-of-the-art methods both qualitatively and quantitatively.

Review of Authentication Systems based on Electroencephalogram

Traditional authentication methods, such as the use of passwords and fingerprints, are susceptible to the risks of theft, loss, and forgery. However, an innovative and secure alternative exists in the form of electroencephalogram (EEG)-based authentication systems, which operate by measuring distinctive brainwave patterns. This particular review undertakes a comprehensive analysis of the current state of EEG-based authentication, delving into its advantages, challenges, and potential future directions. In doing so, we examine the underlying principles governing the acquisition and processing of EEG signals, explore the various techniques employed for feature extraction and classification, and evaluate the performance of existing systems. Moreover, we emphasize the significant advantages offered by EEG-based authentication, including its exceptional accuracy, capacity for liveness detection, and robust resistance to spoofing attempts. Nevertheless, we must also acknowledge and address the various obstacles that must be overcome to facilitate wider adoption of this authentication method, encompassing concerns relating to hardware affordability, user acceptance, and data privacy. Finally, we outline a series of promising research avenues that can potentially address these challenges and unlock the complete potential of EEG-based authentication, thereby enabling secure and convenient access control across a diverse range of domains.

Statistical privacy protection for secure data access control in cloud

Cloud Service Providers (CSPs) allow data owners to migrate their data to resource-rich and powerful cloud servers and provide access to this data by individual users. Some of this data may be highly sensitive and important and CSPs cannot always be trusted to provide secure access. It is also important for end users to protect their identities against malicious authorities and providers, when they access services and data. Attribute-Based Encryption (ABE) is an end-to-end public key encryption mechanism, which provides secure and reliable fine-grained access control over encrypted data using defined policies and constraints. Since, in ABE, users are identified by their attributes and not by their identities, collecting and analyzing attributes may reveal their identities and violate their anonymity. Towards this end, we define a new anonymity model in the context of ABE. We analyze several existing anonymous ABE schemes and identify their vulnerabilities in user authorization and user anonymity protection. Subsequently, we propose a Privacy-Preserving Access Control Scheme (PACS), which supports multi-authority, anonymizes user identity, and is immune against users collusion attacks, authorities collusion attacks and chosen plaintext attacks. We also propose an extension of PACS, called Statistical Privacy-Preserving Access Control Scheme (SPACS), which supports statistical anonymity even if malicious authorities and providers statistically analyze the attributes. Lastly, we show that the efficiency of our scheme is comparable to other existing schemes. Our analysis show that SPACS can successfully protect against Collision Attacks and Chosen Plaintext Attacks.

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.