User Identification Research Articles

Advancing triboelectric human machine interfaces with core-sheath nanocomposite fibres: Enhanced flexibility and motion identification via machine learning

The rapid development of triboelectric nanogenerators (TENGs) has led to sustainable sensing applications as triboelectric interfaces for activity/pattern recognition in human machine interaction (HMI). In this work, core-sheath nanofibres were synthesized and employed as triboelectric materials for constructing new TENGs. These electrospun fibres of novel design consist of a nanocomposite core comprised of polystyrene (PS) and molybdenum disulfide (MoS2) nanoplatelets, and a sheath made of poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP). Compared to high performance TENGs based on neat PVDF-HFP nanofibres tribolayer, these new TENGs achieved open circuit voltage of 1944 V and short-circuit current of 40.3 μA, yielding a total improvement of 284% and 783%, respectively, attributed from the highly efficient charge storage and binding capabilities from the MoS2 nanoplatelets and the PS core. To harness the remarkable performances of these new TENGs, a touch input component was developed, designed to work in conjunction with machine learning algorithms. The triboelectric interfaces exhibited high accuracy in user identification, capable of recognizing individual users with up to 86% average accuracy, under identical press patterns applied by different users. This paves the way for future development of machine learning assisted HMI enabled by TENGs for next generation sustainable communication networks.

Enhancing security in brain–computer interface applications with deep learning: Electroencephalogram-based user identification

Electroencephalogram (EEG) signals have gained widespread use in medical applications, and the utilization of EEG signals as a biometric feature for user identification systems in brain–computer interface systems has recently garnered significant attention. This paper presents a deep learning framework that uses a deep residual neural network (ResNet) for the identification of distinct individuals based on their EEG signals. The proposed framework utilizes continuous wavelet transform to convert one-dimensional EEG signals into two-dimensional spatial images. By incorporating both the frequency and time characteristics of the EEG signals, this model effectively considers and analyses the intricate details present in the data, leading to improved performance. The ResNet model considers the interdependencies between different time instances of the EEG signals. These unique features, collectively, provide sufficient discriminative information to accurately identify individuals. The proposed method achieved an exceptional classification accuracy of 99.73% and an equal error rate of 0.0041 for 64 channels within 109 individuals. The results illustrate the superiority of the proposed method over existing approaches.

Analysis and mining of user behavior on e-commerce platforms based on decision trees and RFM models

User transaction behavior has driven the development of e-commerce. This article reveals the key role of the development of the e-commerce industry and user behavior in driving it through in-depth research on e-commerce sales data. Firstly, by grouping and summarizing user IDs through Pandas, a comprehensive user consumption situation was obtained, including consumption frequency, amount, etc., and user consumption behavior was visualized through visual means. On the basis of constructing effective features, Matplotlib was used to depict the number of users who receive coupons every week and day, providing a basis for the establishment of subsequent coupon distribution prediction models. By splitting the payment dates, an RFM model was constructed to analyze the purchase time period, frequency, consumption amount, and the proportion of each user's time behavior, forming a detailed user profile. Finally, the decision tree model is applied to predict the probability of coupon usage.

An ultra thin, bright, and sensitive interactive tactile display based on organic mechanoluminescence for dual‐mode handwriting identification

AbstractVisible light‐based human–machine interactive media is capable of transmitting electrical readouts to machines and providing intuitive feedback to users simultaneously. Currently, many inorganic mechanoluminescent (ML) materials‐based interactive media, typically ZnS‐loaded phosphors (ZLPs), have been successfully demonstrated. However, organic ML materials‐based solutions were rarely exploited despite their huge merits of strong structural modification, abundant luminescence property, low cost, easy preparation, and so on. Here, we propose a novel interactive tactile display (ITD) based on organic ML materials (Cz‐A6‐dye) and triboelectric nanogenerator, with ultra‐brightness (130% enhancement) and ultra‐low threshold pressure (57% reduction) as compared to ZLPs. The proposed ITD achieves the conversion of weak mechanical stimuli into visible light and electrical signals simultaneously, without extra power supplies. Furthermore, the relationship between the luminous performance of organic ML materials and mechanical force is quantified, benefiting from the uniform ML layer prepared. Enabled by convolutional neural networks, the high‐accuracy recognition (97.1%) for handwriting and identity of users is realized at the same time. Thus, the ITD has great potential for intelligent wearable electronics and classified military applications.image

Cloud Enabled e-Banking Payment Security Implementation using Blockchain Technology

Cloud computing has revolutionized the banking industry by offering flexible and flexible products for the delivery of electronic banking services. However, due to the sensitivity of the central authority, ensuring the security of online transaction is still a critical issue. In this context, blockchain technology is promising by offering decentralization, transferability and cryptographic security. This article presents a new way to increase the security of electronic banking payments by combining blockchain technology with cloud computing. The system plans to use the cloud to host business applications and services, and use blockchain to securely record and verify transactions. Smart contracts are used to streamline the payment process and manage transactions, providing transparency and trust. Additionally, encryption technology is used to encrypt sensitive information and protect the user's identity. Using this solution provides many benefits, including improving the security, transparency and efficiency of electronic banking. Research articles and experimental results demonstrate the effectiveness and applicability of the proposed process in real life. Overall, the integration of blockchain technology with cloud banking provides a solution to solve security issues and increase the reliability of online payments.

EFFICIENT MULTI-AUTHORITY ACCESS CONTROL USING CIPHERTEXT POLICY -HIERARCHICAL ATTRIBUTE BASED ENCRYPTION IN CLOUD STORAGE

The goal of this research is to improve the security of cloud-based data storage systems by presenting the Cloud Secure Storage Mechanism based on Data Dispersion and Encryption (CPHABE). The User Frame, Authority Login, Cloud Server Login, and Data Owner Login are the four separate components that make up the system. By providing file names and requesting transformation keys from the authority for decryption, users start downloads. Requests for transformation keys, upload information, and key creation are handled by the authority. Owner identities, file names, encrypted content, gate types, and storage size graphs are all managed by cloud servers. Partially decrypted data, such as user identities, file names, transformation keys, data owner IDs, and partially decrypted content, is sent in response to user requests. Data owners may use the system to explore files, obtain keys, and apply hybrid encryption methods including Authenticated Encryption (AE) and Key Encapsulation Mechanism (KEM). This all-encompassing strategy guarantees strong encryption, data dispersion, and security measures, offering a stable foundation for cloud storage systems. Keywords: Cloud Storage, Security Mechanism, Encryption

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

Privacy Enhancement Via Dummy Points in the Shuffle Model

The shuffle model is recently proposed to address the issue of severe utility loss in Local Differential Privacy (LDP) due to distributed data randomization. In the shuffle model, a shuffler is utilized to break the link between the user identity and the message uploaded to the data analyst. Since less noise needs to be introduced to achieve the same privacy guarantee, following this paradigm, the utility of privacy-preserving data collection is improved. We propose DUMP ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DUM</u> my- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> oint-based), a framework for privacy-preserving histogram estimation in the shuffle model. The core of DUMP is a new concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dummy blanket</i> , which enables enhancing privacy by just introducing dummy points on the user side and further improving the utility of the shuffle model. We instantiate DUMP by proposing two protocols: pureDUMP and mixDUMP, and conduct a comprehensive experimental evaluation to compare them with existing protocols. The experimental results show that, under the same privacy guarantee, (1) the proposed protocols have significant improvements in communication efficiency over all existing multi-message protocols, by at least 3 orders of magnitude; (2) they achieve competitive utility, while the only known protocol (Ghazi <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , PMLR 2020) having better utility than ours employs hard-to-exactly-sample distributions which are vulnerable to floating-point attacks (CCS 2012).

Fraud Detection and Prevention in E-commerce using Decision Tree Algorithm

Abstract: Fraud detection is an important part of e-commerce because it helps prevent fraud such as illegal transactions, identity theft, and money laundering. Recently, there has been a lot of literature on the application of machine learning algorithms to identify e-commerce fraud. These algorithms work by learning patterns in data that indicate fraud. Pattern checking deals with discovering differences in data, such as unusual products, locations, or behavior outside the norm for certain users, through machine learning. In this project, we propose a decision tree algorithm to detect fraud in e-commerce using newly generated data from various online products on e-commerce sites. In addition to fraud detection, we also provide advice on fraud prevention. We propose a new security model that will prove the user's identity. In this security model, users are required to register their profile with some questions. Our security systems will display relevant images in response to the registration question. The user has to click on the correct answer image within the time limit. We will ask the user 3 questions in graphic format. If the user selects the correct answer, the user will be considered a real user.

AI-driven biometrics for secure fintech: Pioneering safety and trust

AI-driven biometrics is revolutionizing the security landscape in the financial technology (FinTech) sector, enhancing safety measures and fostering trust among users. This review explores the role of AI-driven biometrics in securing FinTech operations, highlighting its benefits and implications. AI-driven biometrics refers to the use of artificial intelligence (AI) algorithms to analyze biological data for authentication purposes. This technology has gained significant traction in the FinTech sector due to its ability to provide a higher level of security than traditional authentication methods. By analyzing unique biological traits such as fingerprints, facial features, and voice patterns, AI-driven biometrics can accurately verify the identity of users, making it difficult for unauthorized individuals to gain access to sensitive financial information. One of the key benefits of AI-driven biometrics in FinTech is its ability to enhance security measures. Traditional authentication methods such as passwords and PINs are increasingly vulnerable to hacking and fraud. AI-driven biometrics, on the other hand, offers a higher level of security by using biological traits that are unique to each individual. This makes it significantly more difficult for fraudsters to gain access to sensitive financial information. In addition to enhancing security, AI-driven biometrics also improves the user experience. By replacing traditional authentication methods with biometric authentication, users can access their accounts more quickly and conveniently, without the need to remember complex passwords or PINs. This not only improves the overall user experience but also reduces the risk of user error and account lockouts. Overall, AI-driven biometrics is pioneering safety and trust in the FinTech sector by providing a higher level of security and enhancing the user experience. As this technology continues to evolve, it is likely to play an increasingly important role in securing financial transactions and fostering trust among users.

Growsight: A Comprehensive CRM and Business Insight Platform for Enhanced Decision Making

Abstract: In the contemporary business environment, the fusion of Customer Relationship Management (CRM) and advanced Business Insights is vital for informed decision-making. This paper presents GrowSight, a cutting-edge CRM and Business Insights platform tailored for sales teams, marketers, CEOs, analysts, and freelancers. Leveraging the Flutter framework in Android Studio and the DhiWise plugin for intuitive UI design, GrowSight ensures a seamless user experience. Key to GrowSight's functionality is its integration of Firebase Google login, providing secure user onboarding. Each user receives a unique Global User ID (GUID) for data integrity. GrowSight supports CSV data maintenance and storage in Salesforce via REST APIs, enabling Business Insights with uploaded CSV files.

Enhance the Security of ATM Machine Access with Face and Fingerprint Recognition

Abstract: The project introduces an innovative approach to enhance the security and accessibility of Automated Teller Machines (ATMs). In this era where financial transactions are increasingly digital and essential, safeguarding ATM access is of paramount importance. This project leverages the power of biometric authentication, combining fingerprint and face recognition technologies to create a robust and reliable access control system. The proposed system allows ATM users to authenticate their identity with a seamless and secure process. Fingerprint recognition provides a high-precision, individualized identification method, while face recognition adds an additional layer of security, ensuring the users identity matches the stored biometric data. This dual-biometric approach significantly reduces the risk of unauthorized access and fraudulent activities at ATMs. Beyond enhancing security, the project contributes to user convenience by streamlining the authentication process, eliminating the need for traditional ATM cards or PINs. Users can access their accounts swiftly and securely, thereby improving the overall ATM experience. Furthermore, the project’s incorporation of state-of-the-art biometric technologies underscores its potential to set a standard for secure ATM access not only in the financial sector but also in various other domains where access control is critical.

Blockchain Enabled Social Network for Detecting Fake Accounts

Front end technology with permanent accounts is employed in online social networks to help people get to know one another. In an effort to maintain connection with everyone, Facebook and Twitter are changing along with the users. OSNs are used by them for planning events, news exchange, interpersonal communication, and even running their own online enterprises. Because of OSNs' quick growth and the abundance of personal data that its users have shared, attackers and imposters have been lured to them with the intention of stealing personal information, spreading disruptive activities, and publishing false information. A further rationale for creating fictitious profiles for hateful accounts. In an attempt to harm the reputation of the person they detest, these users make profiles using their usernames and publish photographs and articles that aren't related to them. to use blockchain technology, Aadhar number verification, and SVM profile classification to create a reliable solution for detecting phoney social network profiles. By analysing several aspects of social media profiles, the SVM classification uses machine learning to discern between real and fraudulent accounts. By verifying Aadhar numbers through legitimate channels, Aadhar number verification is integrated into the registration process, guaranteeing the authenticity of user identities. A decentralised and secure identity management system is made possible by the use of blockchain technology, with smart contracts providing immutable storage for verified user data.

SPAMMER DETECTION AND FAKE USER IDENTIFICATION ON SOCIAL NETWORKS

The project titled "Spammer Detection and Fake User Identification on Social Networks" aims to explore and implement a novel approach to concealing information within digital images while preserving their visual integrity. With the exponential growth of social media platforms like Instagram, the issue of spam and fake user accounts has become increasingly prevalent, posing significant challenges to the integrity and user experience of these platforms. In this project, a comprehensive approach to address the problem of spammer detection and fake user identification on Instagram is proposed. Leveraging machine learning techniques, including Decision Tree Classifier, KNN Classifier, Random Forest Classifier, and Logistic Regression Algorithm, the project aims to develop robust models capable of automatically identifying and flagging suspicious accounts. By analyzing various features such as user behaviour patterns, engagement metrics, and content characteristics, these classifiers will be trained to differentiate between genuine and fake accounts effectively. The project's ultimate goal is to contribute to the enhancement of Instagram's spam detection mechanisms, fostering a safer and more authentic social media environment for users. Key Words: Spam detection, Fake user identification, KNN Classifier, Decision Tree Classifier, Random Forest Classifier, Logistic Regression Algorithm.

Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar

The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.

Enhanced Local Binary Pattern Algorithm for Facial Recognition Using Chinese Remainder Theorem

Current biometrics research focuses on achieving a high authentication success rate for identity management and discussing the threat of various security attacks. Local Binary Pattern (LBP), one of the methods for feature extraction, and Chicken Swarm Optimization (CSO), one of the strategies for feature selection, were used for user identification and authentication. LBP requires high computational time to extract features from the facial images. The Chinese Remainder Theorem (CRT) was used to reduce its computational time by formulating an Enhanced Local Binary Pattern (ELBP). Michael Olugbenga Banji Abolarinwa (MOBA) database was created specifically for this study. 600 frontal facial images of 200 people were collected, each with three images. 360 images were used for training while 240 images were used for testing. MATLAB (R2016a) was used to run the simulation. The time it took to classify the facial images when LBP and CSO were combined and when ELBP and CSO were combined were enumerated. The LBP-CSO achieved a false-positive rate (FPR) of 11.67%, a sensitivity (SEN) of 92.78%, a specificity (SPEC) of 88.33%, a precision (PREC) of 95.98%, and an accuracy of 91.67% in 119.10 seconds at 0.80 thresholds for face recognition. ELBP-CSO obtained an FPR of 5.00%, SEN of 95.00%, SPEC of 95.00%, PREC of 98.28%, and accuracy of 95.00% in 79.16 seconds. The results showed that LBP-CSO took an average of 119.10 seconds and ELBP-CSO took an average of 79.16 seconds. In conclusion, the performance of CSO-ELBP justifies the usage of LBP enhancement with CRT.

From Research to Applications: What Can We Extract with Social Media Sensing?

With the constant growth of social media in our daily lives, a huge amount of information is generated online by multiple social networks. However, what can we actually extract with the science of social media sensing? It is a very challenging task to mine meaningful data out of this vast crowdsourcing volume, which also rapidly changes or ends up being misleading. The scope of this paper is to present different approaches that overcome these challenges and utilize social media information from various sources. This work illustrates applications that: improve the performance of architectural design; preserve the cultural heritage; enhance citizen security; provide early detection for disasters; and discover creeping crisis events. A large variety of analyses are presented, including, among other, disaster or crime event detection, user identity linkage, relevance classification, and community detection techniques. The evaluation of the presented methods is also given in this article, proving that they can be practical and valuable in many applications.

Multimodal Biometric Authentication System Using of Autoencoders and Siamese Networks for Enhanced Security

Ensuring secure and reliable identity verification is crucial, and biometric authentication plays a significant role in achieving this. However, relying on a single biometric trait, unimodal authentication, may have accuracy and attack vulnerability limitations. On the other hand, multimodal authentication, which combines multiple biometric traits, can enhance accuracy and security by leveraging their complementary strengths. In the literature, different biometric modalities, such as face, voice, fingerprint, and iris, have been studied and used extensively for user authentication. Our research introduces a highly effective multimodal biometric authentication system with a deep learning approach. Our study focuses on two of the most user-friendly safety mechanisms: face and voice recognition. We employ a convolutional autoencoder for face images and an LSTM autoencoder for voice data to extract features. These features are then combined through concatenation to form a joint feature representation. A Siamese network carries out the final step of user identification. We evaluated our model’s efficiency using the OMG-Emotion and RAVDESS datasets. We achieved an accuracy of 89.79% and 95% on RAVDESS and OMG-Emotion datasets, respectively. These results are obtained using a combination of face and voice modality.

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

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

One-key based cryptographically generated address for location update in next generation IP mobility

Mobile devices have risen due to internet growth in recent years. The next generation of internet protocol is evolving for mobile devices to generate their addresses and get continuous services across networks to support the enormous number of addresses in network-based mobility. The mobile device updates its current location to its home network and the correspondent users through a binding update scheme in the visited network. Numerous studies have investigated binding update schemes to verify the reachability of the mobile device at its home network. However, most schemes endure security threats due to the incompetence of authenticating user identity and concealing the temporary location of mobile devices. To address these issues, this paper proposes a secure and efficient binding update scheme (One-CLU) by incorporating a one-key-based cryptographically generated address (CGA) to validate and conceal the address ownership of mobile devices with minimal computations. The security correctness of the proposed One-CLU scheme is verified using AVISPA – a model checker. Finally, the simulation and the numerical results showthat the proposed scheme significantly reduces communication payloads and costs for the binding update, binding refresh, and packet delivery.