User Authentication Research Articles

Research on fastDTW and isolation forest-based lightweight gesture authentication

This study explores the use of the accelerometer and gyroscope sensors in an Android mobile phone to capture data during user gesture performance. The recorded data is then processed and analyzed to extract feature values. The authentication of the user's identity may be achieved by this method, which is characterized by minimal constraints. This study initially compares the user's gesture to the template gesture using FastDTW. After that, an adaptive weighted vote determines the closest gesture template. This checks if the user is doing the template category correctly. The second stage extracts effective feature values from authentic user gestures. These feature values include efficient time-frequency domain feature values and the data extreme point spacing-to-length ratio. After that, authentic gestures are used to build the Isolation Forest to verify the user. This approach suggests a reduction in FRR, FAR, and an increase in accuracy, while using a smaller amount of data.

A two-phase spatiotemporal chaos-based protocol for data integrity in IoT

One of the biggest problems with Internet of Things (IoT) applications in the real world is ensuring data integrity. This problem becomes increasingly significant as IoT expands quickly across a variety of industries. This study presents a brand-new data integrity methodology for Internet of Things applications. The “sequence sharing” and “data exchange” stages of the suggested protocol are divided into two parts. During the first phase, each pair of nodes uses a new chaotic model for securely exchanging their identity information to generate a common sequence. This phase’s objectives include user authentication and timing calculations for the second phase of the recommended method’s packet validation phase. The recommended approach was tested in numerous settings, and various analyses were taken into account to guarantee its effectiveness. Also, the results were compared with the conventional data integrity control protocol of IoT. According to the results, the proposed method is an efficient and cost-effective integrity-ensuring mechanism with eliminates the need for third-party auditors and leads to reducing energy consumption and packet overhead. The results also show that the suggested approach is safe against a variety of threats and may be used as a successful integrity control mechanism in practical applications.

Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms

As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detect and block cyber threats. These advanced security mechanisms improve the resilience of 6G networks and ensure the integrity, confidentiality, and availability of critical network assets. IAM protocols exhibit fast user authentication speeds with average authentication time of 0.5 seconds. ZTNA frameworks ensure high network security with a 98% detection rate of abnormal network behaviors

Next-Generation Threat Detection and Mitigation in 6G Wireless Networks Using IAM, ZTNA and Advanced Security Mechanisms

As the deployment of 6G wireless networks looms on the horizon, the imperative to fortify their security infrastructure becomes increasingly pressing. One way to achieve this is through Identity and Access Management (IAM) frameworks to implement strong authentication and authorization mechanisms. Zero Trust Network Access (ZTNA) architectures advocate for a shift towards continuous verification and least-privileged access principles. Secure network segmentation and behavior anomaly detection systems limit the scope of potential breaches. Intrusion Detection and Prevention Systems (IDPS) detect and block cyber threats. These advanced security mechanisms improve the resilience of 6G networks and ensure the integrity, confidentiality, and availability of critical network assets. IAM protocols exhibit fast user authentication speeds with average authentication time of 0.5 seconds. ZTNA frameworks ensure high network security with a 98% detection rate of abnormal network behaviors.

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

The utilization of IoT is expanding across various domains, including telecare, intelligent home systems, and transportation networks. In these environments, IoT devices generate data gathered on remote servers, requiring external users to authenticate themselves to access the data. However, existing authentication protocols for IoT must meet the crucial requirements of speed, security against multiple attacks, and ensuring user anonymity and un-traceability. The main objective of this work is to find lightweight symmetric cryptography-based user authentication protocol tailored for IoT-based applications, focusing on MIM (Man-in-the-Middle) attack prevention, enhanced anonymity, and secure communication between IoT nodes and remote servers via IoT gateways. Existing protocols often lack sufficient defenses against MIM attacks and do not adequately address the need for enhanced user anonymity and secure communication channels within the IoT framework. Our research has identified that authentication techniques based on pairing are susceptible to attacks targeting temporary session-specific data, impersonation, privileged insiders, and offline password guessing. Furthermore, using bilinear pairing in these techniques requires significant computational and communication resources to address the security as mentioned above concerns. A new authentication mechanism must be proposed and designed explicitly for IoT scenarios. The proposed approach exclusively utilizes hash and exclusive-or operations to ensure suitability within the IoT context; thoroughly evaluated the recommended protocol against existing authentication protocols, employing both informal and formal analytical routines like BAN logic, ROR model, and AVISPA simulation. Our findings suggest protocol not only enhances performance but also enhances security. The proposed approach is a tried-and-true strategy for improving security rules in practical Internet of Things (IoT) settings addressing the inherent challenges posed by authentication requirements in IoT environments. The accuracy 98.93%, and Node detection rate 46.57% were improved which is a better outcome.

JAVA Based Quiz Application

The Java-based Quiz Application is a versatile, standalone program designed to operate independently of an internet connection, making it ideal for offline use in various settings such as training sessions, schools, and areas with limited internet access. It boasts a comprehensive set of features for quiz creation, administration, and participation. Key among its features is the offline user authentication system, enabling users to securely create accounts and log in without internet connectivity. This ensures accessibility and autonomy for users regardless of their location or internet availability. The application offers a user-friendly interface for adding various question types, including multiple choice, to offline quizzes, allowing users to customize quizzes to suit their needs and cover diverse topics. Furthermore, users can manage quizzes offline, facilitating real-time adjustments by educators, trainers, or organizers without the need for an internet connection. The application provides players with a seamless offline quiz experience, offering swift performance feedback and navigation to enhance engagement. Users can select quizzes from offline categories, attempt them offline, and receive immediate scoring and feedback. Emphasizing security, the Java Quiz Application implements robust encryption techniques to safeguard user data and ensure confidentiality offline. This commitment to security ensures the protection of sensitive information even in offline environments, maintaining user trust and confidence in the application's integrity. Key Words: Java, No Internet Connection, Offline Quiz, user Friendly

Wind Chat: Robust & Dynamic Flutter Messaging Application

"Wind Chat" is an Android messaging application developed using Flutter and Google Firebase. It enables users to sign in with their Gmail accounts, send friend requests, and engage in real-time messaging. It is a robust messaging application developed using cutting-edge technologies - Flutter and Google Firebase. Flutter, an open-source UI software development toolkit, allows for the creation of natively compiled applications for mobile, web, and desktop from a single codebase. Its expressive and flexible framework ensures a seamless user experience across various platforms. Complementing Flutter, Google Firebase serves as the backend infrastructure, providing a suite of powerful tools for authentication, real-time database management, and cloud storage. Firebase Authentication ensures secure user sign-ins through various methods, while Firebase Real time Database facilitates dynamic and synchronized data updates in real-time. Additionally, Firebase Cloud Storage caters to the reliable storage of multimedia content, a pivotal aspect of Wind Chat's functionality. This integration ensures secure and efficient user authentication and authorization, while also enabling dynamic and responsive data handling, critical for a real-time messaging application like Wind Chat. The combination of Flutter and Firebase empowers "Wind Chat" with a compelling and dynamic user interface, ensuring fluid navigation and an intuitive messaging experience. This fusion of innovative technologies underpins Wind Chat's ability to deliver a feature-rich and user-centric platform for seamless communication.

Event Management to Connect Across Video Conferencing Platforms

Managing events across multiple video conferencing platforms poses challenges in coordination, resulting in inefficiencies and fragmented user experiences. To address these issues, this project develops a cross-platform integration solution, creating a unified platform for seamless management, scheduling, and tracking of meetings. Integration techniques, such as authentication mechanisms and request wrappers, are explored to facilitate interaction with major platforms like Zoom, Microsoft Teams, and Google Meet. The project aims to enhance user workflow efficiency across multiple platforms by introducing a suite of integrated components. The cornerstone is a secure User Authentication system with Single Sign-On support, ensuring streamlined access. A Unified Dashboard serves as a centralised hub for managing meetings, eliminating the need for platform-switching. Cross-Platform Scheduling enables seamless meeting arrangement from a single interface, while Real-Time Updates keep users informed of schedule changes. Additionally, the incorporation of Artificial Intelligence-driven chat for meeting scheduling facilitates intuitive, natural language-based interaction. Together, these components optimise user experience, saving time and effort. Keywords—Meet, Event Management, Zoom, Integration, video conference

A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming

The sharing of mobile network infrastructure has become a key topic with the introduction of 5G due to the high costs of deploying such infrastructures, with neutral host models coupled with features such as network function virtualization (NFV) and network slicing emerging as viable solutions for the challenges in this area. With this in mind, this work presents the design, implementation, and test of a flexible infrastructure-sharing 5G network architecture capable of providing services to any type of client, whether an operator or not. The proposed architecture leverages 5G’s network slicing for traffic isolation and compliance with the policies of different clients, with roaming employed for the authentication of users of operator clients. The proposed architecture was implemented and tested in a simulation environment using the UERANSIM and Open5GS open-source tools. Qualitative tests successfully validated the authentication and the traffic isolation features provided by the slices for the two types of clients. Results also demonstrate that the proposed architecture has a positive impact on the performance of the neutral host network infrastructure, achieving 61.8%-higher throughput and 96.8%-lower packet loss ratio (PLR) in a scenario sharing the infrastructure among four clients and eight users when compared to a single client with all the network resources.

A certificateless signcryption with proxy-encryption for securing agricultural data in the cloud

Precision agriculture (PA) involves collecting, processing, and analyzing datasets in agriculture for an informed decision. Due to the high data storage and application maintenance costs, farmers usually outsource their agricultural data obtained from PA to cloud service providers to leverage cloud services. Nonetheless, serious security concerns arise from using cloud services for farmers. For instance, an attacker can intercept agricultural data and run comprehensive statistical analyses to adjudicate farmers’ financial status, extort money, commit identity theft, etc. As a result, compelling data security schemes have become crucial for secure precision farming, where only legitimate users are required to access the agricultural data outsourced to the cloud. This article presents a certificateless signcryption scheme with proxy re-encryption (CLS-PRE) for secure access control in PA. An in-depth security analysis proves that the CLS-PRE scheme is secure in the Random Oracle Model. Detailed performance evaluation also shows that the scheme can reduce the time required to signcrypt and unsigncrypt messages and lower communication overhead.

A Secure VM Placement Strategy to Defend against Co-residence Attack in Cloud Datacentres

With the increasing number of co-residence attacks, the security of the multi-tenant public IaaS cloud environment has become a growing concern. The co-residence attacker creates a side channel to retrieve the secured data. These attacks help the adversary to leak out the sensitive information of the user with whom it is co-located. This paper discusses a secured VM placement technique, Previous Server and Co-resident users First (PSCF), which focuses on facilitating security against the co-residence attack by minimizing the probability of co-locating the malicious user with the authentic user. Co-location resistance and core utilization metrics are utilized to evaluate the algorithm’s performance. The proposed method is simulated, and the result is analysed and compared with existing approaches like Best Fit, Worst Fit, PSSF, and SC-PSSF. It is observed that the proposed approach furnished maximum co-location resistance of 74.32% and a core utilization of 82.63%. Further, the algorithm has shown significant performance in balancing the load and energy consumption. The result has reduced the probability that malicious users co-located with the authentic one, thus reducing the security breach of confidential information.

Application of IoT audio technology based on sensor networks in English speaking teaching system

In the era of information technology, the exponential growth of data volume has compelled users to entrust their data to database service providers for efficient management. While traditional methods such as user authentication, security auditing, and access control have been implemented to safeguard data security across databases, cloud servers may face security challenges due to excessive internal administrator permissions. The audio technology of the Internet of Things (IoT) offers numerous advantages, including high bandwidth, short data transmission time, long transmission distance, and cost-effectiveness. It has found widespread applications in military, technological, industrial, and everyday life settings. With the advancements in modern information technology and the development and enhancement of wireless radio frequency technology in the IoT sector, IoT-based wireless audio interaction systems utilizing Wi-Fi have gradually become integrated into people's lives. These systems have enriched people's daily experiences and are increasingly popular among consumers. This paper, based on the theory of functional applied linguistics, combs the basic theoretical framework and the practical application prospect of functional Applied Linguistics of systemic language, especially emphasizes the important influence and application enlightenment of systemic functional linguistics on language teaching, and combines the "Recent Development Zone" The basic concepts of contemporary language and literature education, such as scaffolding, are summarized and analyzed. The paper establishes a comprehensive theoretical research framework for college spoken English teaching, and through the comprehensive theoretical analysis of communicative teaching methods and practical exploration of some problems that students may appear, it puts forward more effective and targeted specific solutions.

Adversarially robust and real-time DDoS detection and classification framework using AutoML

ABSTRACT Denial of Service (DoS) attacks target the availability part of the CIA triad (Confidentiality, Availability, and Integrity). A special category of these attacks is the Distributed DoS (DDoS) attack, where the attacker uses a network of compromised systems called a botnet to flood a target server with requests and refuses to serve legitimate users. DDoS attacks can cost an organization millions of dollars in terms of lost revenue, remediation costs, and damage to brand reputation. Hence, all organizations need speedy real-time detection of DDoS attacks. This work presents a DDoS detection and classification framework using the flow-based approach for feature engineering and the AutoML technique. Our detection system is trained on the latest DDoS datasets – CIC-DDoS 2019 and CIC-IDS 2017, which contain various categories of DDoS attacks. We use various tools to perform adversarial attacks on our trained model. We retrain our models using adversarially crafted network packet captures and then test our models for robustness against practical adversarial attacks that an attacker might use to evade detection. Finally, we deploy our model in real-time using a GUI-based tool. Our model achieves a validation accuracy of 99.9% and a low false positive rate of 0.05%.

Fashion Finder : Ai-Powered Image Analysis and Online Shopping Integration App

The proposed mobile application aims to revolutionize the fashion industry by providing users with a seamless way to discover and purchase clothing items through image recognition technology. The app allows users to either capture images of clothing items through their device's camera or upload images from their stored files. Leveraging machine learning and computer vision algorithms, the app identifies the corresponding clothing items within the images. Upon identification, the app interfaces with e-commerce platforms such as Amazon and Flipkart to search for similar items available for purchase. The development process involves extensive research, design, and implementation of features including image recognition, camera/file upload functionality, API integration with e-commerce platforms, user authentication, and additional user-friendly features. Through rigorous testing, deployment on major app stores, and continuous maintenance, the app aims to offer a seamless and engaging experience for fashion enthusiasts while adhering to user privacy and data security standards.

MotionID: Towards practical behavioral biometrics-based implicit user authentication on smartphones

Traditional one-time authentication mechanisms cannot authenticate smartphone users’ identities throughout the session — the concept of using behavioral-based biometrics captured by the built-in motion sensors and touch data is a candidate to solve this issue. Many studies proposed solutions for behavioral-based continuous authentication; however, they are still far from practicality and generality for real-world usage. To date, no commercially deployed implicit user authentication scheme exists because most of those solutions were designed to improve detection accuracy without addressing real-world deployment requirements. To bridge this gap, we tackle the limitations of existing schemes and reach towards developing a more practical implicit authentication scheme, dubbed MotionID, based on a one-class detector using behavioral data from motion sensors when users touch their smartphones. Compared with previous studies, our work addresses the following challenges: ① Global mobile average to dynamically adjust the sampling rate for sensors on any device and mitigate the impact of using sensors’ fixed sampling rate; ② Over-all-apps to authenticate a user across all the mobile applications, not only on-specific application; ③ Single-device-evaluation to measure the performance with multiple users’ (i.e., genuine users and imposters) data collected from the same device; ④ Rapid authentication to quickly identify users’ identities using a few samples collected within short durations of touching (1–5 s) the device; ⑤ Unconditional settings to collect sensor data from real-world smartphone usage rather than a laboratory study. To show the feasibility of MotionID for those challenges, we evaluated the performance of MotionID with ten users’ motion sensor data on five different smartphones under various settings. Our results show the impracticality of using a fixed sampling rate across devices that most previous studies have adopted. MotionID is able to authenticate users with an F1-score up to 98.5% for some devices under practical requirements and an F1-score up to roughly 90% when considering the drift concept and rapid authentication settings. Finally, we investigate time efficiency, power consumption, and memory usage considerations to examine the practicality of MotionID.

Malicious Social Bot Using Twitter Network Analysis in Django

Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes’ theorem, and the indirect trust is derived from the Dempster– Shafer theory (DST) to determine the trustworthiness of each participant accurately. Finally, we shown the user tweet data in terms of graph visualization of bar chart and pie chart of the system. Experimental results shown the better performance of the system.

A robust and effective 3-factor authentication protocol for smart factory in IIoT

Smart factory, as an intelligent application of industrial internet of things (IIoT), significantly enhances the efficiency of industrial processes while also reducing resource waste. In a system where security is crucial, the user authentication mechanism is essentially designed to prevent a range of security issues, including production failures caused by illegal intrusions from hackers. To verify the user’s identity and ensure data be accessed with a secure session key, a large number of authentication protocols have been provided for IIoT. However, existing newly alternatives show deficiency either in terms of superior performance or robust security. To get a better balance of security and efficiency, this paper designs a robust and effective 3-factor user authentication protocol. Then this paper shows the detailed security analyses and automated verification by the Proverif tool, where the ProVerif tool used in this paper can detect whether the eCK adversary with stronger attack ability can break the protocol, while the general ProVerif can only detect threat of the Dolev–Yao adversary with weaker attack ability to the protocol security. Subsequently, this paper presents performance comparison which indicates that the proposed protocol can be superior to those newly alternatives. Especially, the comparison results show that the computation cost consumed in the proposed protocol can be reduced by 62.2% than the average cost of all six compared alternatives. Lastly, the evaluation results on the energy consumption and the network delay indicate the practicability of proposed protocol for smart factory.

Personalized user authentication system using wireless EEG headset and machine learning

In the realm of authentication, biometric verification has gained widespread adoption, especially within high-security user authentication systems. Although convenient, existing biometric systems are susceptible to a number of security vulnerabilities, including spoofing tools such as gummy fingers for fingerprint systems and voice coders for voice recognition systems. In this regard, brainwave-based authentication has emerged as a novel form of biometric scheme that has the potential to overcome the security limitations of existing systems while facilitating additional capabilities, such as continuous user authentication. In this study, we focus on a data-driven approach to Electroencephalography (EEG)-based authentication, guided by the power of machine learning algorithms. Our methodology addresses the fundamental challenge of distinguishing real users from intruders by training classification algorithms to the unique EEG signatures of every individual. The system is characterized by its convenience, ensuring real-time applicability without compromising its efficiency. By employing a commercially available single-channel EEG sensor and extracting a set of 8 power spectral features (delta [0–4 Hz], theta [4–8 Hz], low alpha [8–10 Hz], high alpha [10–12 Hz], low beta [12–20 Hz], high beta [20–30 Hz], low gamma [30–60 Hz], high gamma [60–100 Hz]), a commendable mean accuracy of 85.4% was achieved.

The Squirrel Prover and its Logic

Security protocols are widely used today to secure transactions that take place over public channels like the Internet. Common uses include the secure transfer of sensitive information such as credit card numbers, or user authentication on a system. Because of their presence in many widely used applications ( e.g. electronic commerce, government-issued ID), developing methods and tools to verify security protocols has become an important research challenge. Such tools help increase our trust in protocols, and hence on the applications that rely on them.

TrapCog: An Anti-Noise, Transferable, and Privacy-Preserving Real-Time Mobile User Authentication System With High Accuracy

The authentication technology of mobile device users has been studied for decades. To balance security, privacy, and usability, motion sensors-based user authentication methods are widely investigated in recent years. However, existing studies meet the problems such as scarcity of training samples, underutilization of data, poor de-noising ability, insufficient transferability, privacy leakage, and low accuracy. To overcome these difficulties, we propose a system, called for Transferable, Real-time, Anti-noise, and Privacy-preserving mobile user COGnition., with the following capabilities: 1) In the phase of data collection, can eliminate man-made noise (mislabeling) through differential training based on down-sampling. 2) In the model training stage, the siamese neural network with Long Short-Term Memory (LSTM) as the sub-network is used to achieve sufficient coverage of sample patterns and the transferability of the model. 3) In the phase of real-world authentication, the privacy of the user is tremendously protected through end-side model deployment and local authentication. Experimental results on a dataset composed of 1,513 users with real-world noise show that TRAP COG has high accuracy and strong transferability, which is much better than state-of-the-art studies.