User Security Research Articles

Navigating perilous seas: unmasking online banking frauds, perceived usefulness, fear of cybercrime and distrust in online banking

PurposeThe purpose of this study was to examine the relationship between online banking fraud experience and fear of cybercrime and distrust of online banking services, and to understand how perceived usefulness of online banking moderates the relationship.Design/methodology/approachThe number of respondents involved in this study was 271 people from the Central Java region, Indonesia. Statistical analysis was performed using Jeffreys’s Amazing Statistics Program software to examine the relationships and interactions between the variables studied.FindingsExperience of online banking fraud is positively related to fear of cybercrime and distrust of online banking services. Perceived usefulness of online banking moderates the relationship between online banking fraud experience and fear of cybercrime and distrust of digital payments. Perceived usefulness is negatively related to the level of distrust of online banking services.Research limitations/implicationsOverall, the implications of this study underscore the importance of dealing with the risks of cybercrime in online banking services. By focusing on security, user awareness and the role of perceived usefulness, banking service providers can create a safer and more trusting environment for users of online banking services. This also contributes to the development of more innovative services and can increase customer satisfaction and trust.Practical implicationsThe practical application of these findings is important for financial institutions and online banking service providers. Companies must improve cybersecurity with the latest technology and provide education about online security practices. Transparent communication and better customer service will help overcome customer fears. Compliance with security regulations and technological innovation is also important to protect online banking services. With these steps, customer security and trust can be improved, and the adoption of online banking services will increase widely.Social implicationsThe social implications of this research are increasing public awareness about cybersecurity, consumer protection and strengthening trust in online banking services. With joint efforts, a safer and more trusting environment in using online banking services can be realized.Originality/valueThe originality of this research lies in the use of perceived usefulness of online banking as a moderating variable to reduce the negative impact of online banking fraud experience. With a focus on the psychological effects of customers experiencing fraud, this research seeks to rebuild trust and improve the security of online banking services.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Reverse Engineering for Static Analysis of Android Malware in Instant Messaging Apps

Malware poses a significant threat to Android devices due to their high prevalence and vulnerability to attacks. Analyzing malware on these devices is crucial given the persistent and sophisticated threats targeting Android users. Static analysis of Android malware is a key approach used to detect malicious software without executing the application. This method involves meticulously examining the application's source code or binaries to identify signs of suspicious or harmful activities. The research methodology consists of three stages. The first stage involves collecting malware samples spread through instant messaging applications. The second stage employs reverse engineering, where APK files are decompiled to extract their contents. Following this, a static analysis is conducted, focusing on the AndroidManifest.xml file and the source code to identify the behavior and potential threats posed by the malware. The static analysis results revealed that Android malware often requests sensitive permissions to access personal data, such as receiving, reading, and sending SMS, as well as accessing location and contacts. Further analysis uncovered that after acquiring this data, the malware transmits it to the Telegram API via authenticated HTTP requests using specific tokens and chat_ids. These findings highlight that the permissions requested by the malware are designed to clandestinely collect and export personal data, posing a severe threat to the privacy and security of Android users.

Data Privacy in Social Media: Awareness and Comfortability of Mathematical Computing Course Session I 2023/2024 Sultan Abdul Halim Mu'adzam Shah Polytechnic’s Students

The increasing integration of social media into daily lives of individuals has raised significant concerns about data privacy. As users actively engage with various platforms, the extent to which they are aware of the privacy implications and their comfort levels with the use of personal data become vital issues. This study aimed to identify social media platforms that are often used by students, their level of awareness towards social media data privacy policy and their comfortability towards data privacy by social media companies. The respondents in this study comprises of 106 male and female students. The data are analyzed descriptively by mean, frequency and percentage. The level of awareness towards social media data privacy policy is high with average mean score 3.94, WhatsApp is the often-surfed social media platform by score of 89.62% and most students were highly comfortable with how educational social media platform used their data with mean 3.85. This study proposes the necessity of a diverse approach involving education, user empowerment, and technological advancements to enhance data privacy awareness and comfort levels among students in the realm of social media and prepare to a more secure and responsible online environment for the next generation users

Analisis Hukum Penerapan Blockchain dan Internet of Things (Iot) pada Perlindungan Data Pribadi dalam Transaksi Bisnis E-Commerce di Indonesia

Today, e-commerce is one example of continuous development in technology and information. However, there are some problems with its implementation. One of them is a cyber security system that regulates e-commerce user data which can leak and misuse personal data. In this case of personal data leakage, increasingly advanced technological advances are clearly the cause. This causes problems in society and legal processes. Service users can sue e-commerce companies if personal data is misused. This is because users feel disadvantaged due to misuse of this data. This research uses empirical juridical as a research method, with descriptive analytical research specifications. The research results show that the world of civil law has changed privacy due to technological advances. Studies show that security threats and strategies affect the security of e-commerce users' personal data.

Physical-layer security for primary users in 5G underlay cognitive radio system via artificial-noise-aided by secondary users

The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ( P int ) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary OP, thereby contributing to advancements in IoT security and spectrum management.

Scam Call Detection Using NLP and Naïve Bayes Classifier

Financial fraud, particularly credit card fraud, is a pressing concern in the realm of digital transactions. The number of phone scams is increasing daily as con artists use phone calls to target victims for nefarious ends. Individuals are falling for con artists' proposals, becoming victims and giving up their personal information, leaving them open to abuse. Effective detection techniques are becoming more and more necessary. In this study, we offer an efficient approach to scam call identification utilizing speech-to-text libraries and the machine learning technique Naïve Bayes classifier. Our technology, which translates voice to text, uses this text to evaluate conversations in real time. It looks for trends and suspicious phrases that point to attempted scams, including asking for credit card numbers, passwords, or other sensitive information. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. Keyword: Spam Detection, Naïve Bayes, Natural Language Processing, Machine Learning.

ANDROID HEALTHCARE APPLICATION

The healthcare systems is pivotal in improving the accessibility and management of healthcare services. This paper focuses on the creation of a comprehensive appointment management system to streamline various healthcare interactions. Modern healthcare challenges such as scheduling appointments, ordering medications, and accessing health information necessitate an integrated approach. The proposed system includes modules for user registration, lab test management, medication management, doctor search and appointment booking, health education articles, order tracking, and user logout functionalities. Utilizing user-friendly interfaces and robust backend infrastructure, the system ensures secure user registration, easy browsing of lab tests and medications, efficient search for specialist doctors, convenient appointment booking, access to health articles, and order tracking. Key features include personalized user profiles, real-time appointment scheduling, and secure data storage using a SQLite database. Keywords: Healthcare management, Mobile Applications, Software Engineering

Analisis Keberhasilan Sistem Informasi Sentuh Tanahku Badan Pertanahan Nasional Menggunakan Metode ISSM

The use and usefulness of information technology is an important issue in today's life. This can be seen as a way of using technology to support human activities in various sectors or fields, including government and user security. Of course, the use and usefulness of information technology in various agencies is very necessary to support the implementation of better government programs. The use and utilization of technology in the government environment can be carried out with the help of a research information system. Research is located at two government information agencies: Lubuk-Linggau City National Land Agency; The information system used by the National Land Agency is called Touch My Land based on Android. The implementation of an information system is said to be successful if it can be proven that the information system produces significant positive impacts or benefits for some of its users. The DeLone and McLean evaluation concept is considered a successful information system model. The research method used in this research is a quantitative method with a sample size as many as 61 people from National Land Agency. This research uses six variables and nine hypotheses. The sixth variable consists of information quality, system quality, service quality, usage intention, user satisfaction and net profit. The research results show that several research hypotheses are accepted because the t-statistic value is greater than 1.96.

“SmartEduHub: Empowering Education with Advanced Features”

" SmartEduHub" is an innovative platform designed to transform the educational experience by integrating advanced technologies and a comprehensive range of features. It aims to enhance learning and streamline administrative processes, providing personalized tools for students, educators, and administrators. The platform ensures a dynamic and interactive educational journey with secure user authentication, real-time updates, and cutting-edge learning management systems.

Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms

Context: Cloud-based services are increasingly central in financial technology, enabling scalable and efficient transactions. However, they also heighten vulnerability to fraud, challenging the security of online financial activities. Traditional fraud detection struggles against sophisticated tactics, highlighting the need for advanced, cloud-compatible solutions. Objectives: This study assesses machine learning (ML) algorithms' ability to detect fraud in cloud environments, focusing on Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and XGBoost (XGB). It uses a comprehensive dataset to determine which ML model best identifies fraudulent transactions, aiming to optimize these models for accuracy, precision, and efficiency in real-time detection. Results: The XGBoost model outperformed others in fraud detection accuracy, with Random Forest and Decision Trees also showing high effectiveness. These models were particularly good at balancing precision and recall, minimizing false positives, and accurately identifying fraud in complex transaction patterns. Conclusion: ML, especially ensemble and boosting models like XGBoost and Random Forest, offers a strong approach to detecting transaction fraud in cloud-based financial systems. Their capacity to handle vast data volumes and adapt to new fraud patterns enhances cloud transaction security. Implication: The study provides a guide for financial and cloud services to implement sophisticated fraud detection systems. It emphasizes the importance of continual ML innovation to tackle digital finance fraud, suggesting that adopting these advanced ML models can significantly reduce transaction fraud risks, ensuring a secure, efficient, and trustworthy platform for users.

Marketing Channels for Contract Financing Control of High-tech Listed Companies: Basis for Strategic Plan

Abstract. China's high-tech enterprises have experienced exponential growth in both number and market value, with significant contributions to innovation-driven development, as evidenced by the proliferation of listed companies on the Science and Technology Innovation Board and the Beijing Stock Exchange. This study delved into an assessment of corporate governance, internal control management, and risk management within high-tech enterprises in China, with a specific emphasis on contract financing control. Through a combination of analysis of publicly disclosed data, questionnaire surveys, and examination of internal control documents, the research aimed to provide targeted recommendations for enhancing internal control mechanisms and optimizing contract financing-related management systems. The findings highlighted strong compliance with domestic and international laws and regulations among the 10 selected high-tech listed companies, demonstrating robust performance across parent and subsidiary operations. While internal control documents were comprehensive, areas for improvement were identified. Despite favorable impacts of marketing channels on contract financing control strategies, challenges persisted, including identifying risk sources and addressing issues related to contract compliance, digital technology application, and customer security concerns. Notably, challenges in financial control marketing channel construction encompassed cash flow management, customer credit evaluation, bad debt, and default risks.

Pengaruh Kualitas Pelayanan terhadap Kepuasan Nasabah pada PT. Bank Rakyak Indonesia (Persero) Tbk.Unit Kota Ambon

This study aims to determine the quality of service to customer satisfaction at PT. Bank Rakyat Indonesia. This type of research uses a qualitative approach and produces descriptive data because it intends to explore and appreciate an object. The data collection technique uses observation/observation techniques and questionnaires (questioners). The satisfaction questionnaire was compiled using satisfaction indicators, namely tangibles, emphaty, reliability, responsiveness, assurance Data analysis was carried out descriptively on the data obtained in the field. Based on the results of the data analysis described by the author in the form of the table above, it can be seen that the quality of service at PT. Bank Rakyat Indonesia (Persero) Tbk. Kota Ambon Unit has not been maximized because there are still customers who are not satisfied with the services provided. services provided by BRI Ambon City Unit. The average item is 2.08. The transaction was 2.78 and this shows that this study is in the fairly good category. The bank guarantees security for customers in making transactions. The average item is 4.1, indicating that the research is included in the very good category and this means that the bank is able to ensure the safety of every customer who makes a transaction

Phishing detection tool for financial emails

Phishing attacks have emerged as a significant cybersecurity threat, especially within the financial domain, where email serves as a prevalent vector for malicious activities. This project report outlines the development and evaluation of a Chrome browser extension tailored to bolster email security, specifically focusing on detecting and mitigating phishing attempts in financial communications. The study begins with a thorough examination of the techniques and tactics used by phishing attackers in financial email campaigns. A sophisticated awareness of the changing landscape of phishing assaults is required to develop successful defences. The key contribution of this work is the development of a powerful Chrome browser extension that easily integrates with common email services in the financial sector. This extension harnesses a blend of machine learning algorithms, URL analysis, and user-friendly interfaces to identify potentially malicious financial emails. It rigorously scans email content, scrutinizing hyperlinks and attachments for signs of phishing. The extension evaluates URL reputations, checks for typical phishing indicators, and examines email headers. In case any suspicious elements are identified, users receive real-time alerts, enabling them to make informed decisions about the safety of financial emails. To ensure the extension’s accuracy and effectiveness within the financial context, an extensive dataset of real-world phishing emails and legitimate financial messages is employed for training and testing. The report offers a comprehensive analysis of the extension’s performance, emphasizing its capability to detect and prevent phishing attempts in financial emails while minimizing false positives. In addition to its detection capabilities, the extension actively promotes user awareness and education regarding phishing threats within the financial sector. It provides guidance on recognizing phishing red flags and offers educational resources to enhance user security practices. This work concludes with an evaluation of the extension’s usability and its potential impact on enhancing email security within the financial domain. It underscores the critical role of proactive measures, such as this extension, in addressing the evolving threat landscape of phishing attacks in the digital age, particularly within financial communications.

Detecting malicious DoH traffic: Leveraging small sample analysis and adversarial networks for detection

In light of the escalating frequency of DNS attacks, it is imperative to bolster user security and privacy through the encryption of DNS queries. However, conventional methods for detecting DNS traffic are no longer effective in identifying encrypted traffic, particularly with the utilization of the DNS-over-HTTPS (DoH) protocol, which employs secure HTTPS for DNS resolution. To confront this challenge, we propose a novel model for detecting malicious DoH traffic, named DoH-TriCGAN, which distinguishes between non-DoH, benign DoH, and malicious DoH traffic. DoH-TriCGAN employs a conditional generative adversarial network comprising three network components, for which we only provide additional information to the generator. We extracted different small sample datasets and large sample dataset from the CIRA-CIC-DoHBrw-2020 dataset, to evaluate the efficiency and effectiveness of the proposed DoH-TriCGAN model, and compared the quality of the generated synthetic data. To establish a benchmark, we utilized the six metrics – accuracy, precision, recall, F1-score, ROC_AUC, and PR_AUC – to assess the performance of our model. The results demonstrate our proposed model outperforms the other five models (RF, XGBoost, BiGRU, Autoencoder, Transformer), showing the best performance particularly in scenarios with limited training samples, while also demonstrating data expansion capabilities by generating high-quality synthetic data to address the issue of insufficient network traffic.

Capacity Limit Measuring Tool on Children's School Bags Using Esp32 Microcontroller and GPS Tracker

Backpacks are the main choice for students as a means of carrying school supplies and materials, but often they do not pay attention to the weight of the load they carry every day, potentially causing injuries and pain in the back. This study aims to design and implement a capacity limit measuring device on children's school bags using the ESP32 microcontroller as the control center. The tool is equipped with a 10kg load cell sensor to accurately measure the weight of the bag, as well as a keypad as a user interface to easily enter the child's weight. Safety is also considered with the addition of a buzzer as an alarm if the weight of the bag's capacity exceeds the set limit. GPS trackers are also integrated to monitor the geographical location of bags, allowing parents to monitor the movement of children and the location of bags directly through Telegram bots. With ESP32 as the brain, this tool provides a comprehensive solution to maintain the health and safety of children in carrying their school load. This tool also utilizes Telegram technology to provide direct notifications about the weight and location of children's bags to parents or other users. Telegram bots connected to this tool automatically transmit this information, providing additional convenience and security for users.

AHBSMO-DRN: Single Device and Multiple Sharing-Based Geo-Position Spoofing Detection in Instant Messaging Platform

In recent years, location check-in on mobile components is a trending topic over social media. At the same time, hackers grasp the geographical position (geo-position) data that destruct the security of users. Hence, it is crucial to detect the originality of geo-position. A plethora of methods have been developed for geo-position spoofing identification that depends on geo-position data. Nonetheless, such techniques are incapable in terms of missing prior data or insufficient of large samples. To counterpart this issue, an effective model is invented to detect spoofing activity by Adaptive Honey Badger Spider Monkey Optimization_Deep residual Network (AHBSMO-based DRN). Here, neuro camera footprint refining is performed using Neuro Fuzzy filter and extracted footprint image obtained while considering the input and spoofed image are fused using Pearson correlation coefficient. Meanwhile, geo-tagged value of input image and spoofed image is also fused based on same Pearson coefficient. Finally, fusion is performed and then, spoofing detection is accomplished by comparing the Discrete Cosine Transform (DCT) foot print of two images to find if the input image is spoofed or not. Moreover, AHBSMO-based DRN model has gained outstanding outcomes in regard of accuracy of 0.921, True Positive Rate (TPR) 0 of 0.911, and False Positive Rate (FPR) of 0.136.

A review of deep learning based malware detection techniques

With the popularization of computer technology, the number of malware has increased dramatically in recent years. Some malware can threaten the network security of users by downloading and installing, and even spreading widely on the Internet, causing consequences such as private data leakage in the operating system, extortion, and network paralysis. In order to deal with these threats, researchers analyze malicious samples through various analysis techniques, which are usually divided into static and dynamic analysis based on the principle of whether the code needs to be executed or not. This paper analyzes in detail several classical methods of feature extraction in malware detection techniques. With the technological development of artificial intelligence, deep learning is gradually being introduced into malware detection, which does not require the identification of professional security personnel and greatly improves the generalization ability of detection. In the paper, text-based detection methods, image visualization-based detection, and graph structure-based detection techniques are reviewed according to different feature extraction methods. In addition, the paper compares 26 datasets that have been commonly used in recent years applied in the research field and explains the main contents and specifications of the datasets. Finally, a summary and outlook of the malware research field is given.

Multi-protocol updating for seamless key negotiation in quantum metropolitan networks

Quantum key distribution (QKD) can provide long-term security for numerous users. Currently, quantum networks are still in the early stages of small-scale deployment, most of which can only support a single QKD protocol (QKDP). However, with the advancement of various QKDPs, a single-protocol quantum network is no longer sufficient to meet the demands of multiple users, prompting the emergence of multi-protocol quantum networks. Nevertheless, the transition from a single-protocol to a multi-protocol quantum network still faces many challenging issues, such as key negotiation interruptions due to device initialization and channel calibration during protocol updating. To address the quantum key negotiation interruption problem, we propose a seamless key negotiation oriented multi-protocol updating algorithm in this work, which can fulfill the protocol updating requests of different users in quantum metropolitan networks. Furthermore, to better improve network performance while meeting diverse user demands, we propose four heuristic algorithms for optimal QKDP recommendation, focusing on their applications for multi-protocol updating in different types of typical networks. We perform the simulations with different QKDP recommendation algorithms to analyze the impact of the cache time of the key cache area on the key negotiation interruption probability and the time resource utilization. Simulation results demonstrate that the proposed algorithm can reduce the key negotiation interruption probability by 77.7% while increasing the time resource utilization by 15.3% compared to no key cache area.

A secure user authentication scheme for crypto-wallet in IoT environment

A secure user authentication scheme for crypto-wallet in IoT environment