Mobile Security Research Articles

Home blood pressure monitoring and mobile health application practices among pregnant persons with and at risk of hypertensive disorders of pregnancy.

To examine training in and current home blood pressure monitoring (HBPM) practices in pregnancies with hypertensive disorders of pregnancy (HDP) or risk factors for HDP; and to explore interest in using mHealth applications for communicating with healthcare providers. A cross-sectional survey was conducted on HBPM practices/training and interest in using mobile health (mHealth) applications among pregnant persons at an academic medical center. The main outcomes measured were the proportion of participants who received training for HBPM, and the proportion interested in using mHealth applications for communication with providers related to managing HDP. Of the 103 participants, 41 (39.8%) used HBPM. Of those who did HBPM, 36.6% received training from their healthcare providers. Training more often included instructions on positioning and how to perform HBPM, and rarely included advice on validated HBPM devices. Nearly all participants (98.1%) owned a smartphone, and 19.4% had a smartwatch. A significant majority expressed interest in using a secure mobile app for communication with providers (78.6%), and 88.3% were interested in wearing a smartwatch for remote monitoring. There were no significant differences in interest levels based on race, age, residence, or education, while there was higher interest (100% vs. 67%) in mobile apps for communication among those with HDP vs. risk factors for HDP. Despite a low level of training for HBPM, there was high interest in using mHealth applications. Leveraging mHealth technology could help overcome the challenges of providing training for HBPM.

A Review on Secure Authentication Mechanisms for Mobile Security

Cybersecurity, complimenting authentication, has become the backbone of the Internet of Things. In the authentication process, the word authentication is of the utmost importance, as it is the door through which both Mr. Right Guy and Mr. Wrong Guy can pass. It is the key to opening the most important and secure accounts worldwide. When authentication is complete, surely there will be passwords. Passwords are a brain-confusing option for the user to choose when making an account during the registration/sign-up process. Providing reliable, effective, and privacy-preserving authentication for individuals in mobile networks is challenging due to user mobility, many attack vectors, and resource-constrained devices. This review paper explores the transformation and modern mobile authentication schemes, categorizing them into password, graphical, behavioral, keystroke, biometric, touchscreen, color, and gaze-based methodologies. It aims to examine the strengths and limitations focused on challenges like security and usability. Standard datasets and performance evaluation measures are also discussed. Finally, research gaps and future directions in this essential and emerging area of research are discussed.

A Secure and Efficient Mobile ID Framework for Authentication with Enhance ECC

A Secure and Efficient Mobile ID Framework for Authentication with Enhance ECC

Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning

It is widely acknowledged that digital transformation provides the opportunity for business process improvements. We can see many businesses, specifically in Indonesia's private and public sectors, leveraging technology to provide better service to their stakeholders. This research seeks insight into stakeholders’ engagement in digital transformation in the Indonesian healthcare system, namely the National Health Security Mobile application (Mobile JKN). This study employs a quantitative method to analyze user sentiment from Google Play reviews. Firstly, user reviews are extracted, preprocessing steps are applied, and machine learning-based sentiment labeling is employed afterward to categorize them into positive, neutral, and negative sentiments. The machine labeling process is carried out in document-level analysis, meaning user reviews represent their overall sentiment. Subsequently, we study the most prominent words using Word Cloud to determine the topics mainly discussed in positive and negative sentiments. The result is that 56.10% of reviews from 7 June 2016 to 14 July 2024 contain positive sentiments, while 43.17% contain negative sentiments. Neutral reviews contribute the most minor proportion, making up only 0.73%. The most prominent words in positive sentiment reviews, such as easy, sound, and helpful, suggest the user perception of the National Health Security Mobile as being easy to use and successfully accommodating the user's needs. In contrast, the words application, update, complex, login, code, verification, register, open, use, and error dominate the negative reviews, indicating users had difficulties logging in and registering user accounts, mainly related to frequent updates and Time Password errors.

Improved Mobile Security Based on Two‐Factor Authentication

ABSTRACTDespite password‐based security schemes offering increased protection against unwanted access, two‐factor authentication (TFA) is still not widely used primarily because users find additional login steps to be exhausting. To solve this challenge, SoundSignature, a unique TFA technique, is introduced that is intended to be both deployable and user friendly. It verifies that the user's phone is in close proximity to the login device by recording background noise using the microphones on the device. By mimicking the seamless experience of password‐only authentication, this technique enhances security without requiring direct user intervention. Our experimental research demonstrates in both indoor and outdoor scenarios, as well as those in which a phone is carried in a pocket or handbag, ambient noise serves as an effective classifier for device proximity. SoundSignature, being easy to set up and compatible with most devices and browsers without the need for additional plugins, stops attackers from trying to impersonate or predict by utilizing a complicated connection between acoustic propagation mechanisms and the constant frequency response of hardware components. A prototype implementation of SoundSignature, which attains an average accuracy rate of about 90.2% with a minimal equal error rate of 8.5%, validates the system's robustness and effectiveness in five different environments.

Secure Cluster Based Routing Scheme for Neuro‐Fuzzy Assisted MANET in 5G

ABSTRACTThe Fifth Generation (5G) of wireless networks has increasingly focused significantly on energy‐saving methods for mobile communication systems. The objective of combining MANET with 5G communication is to achieve a high data throughout while simultaneously reducing latency and cost. The main issues with MANET are elevated energy use, security, and reduced longevity. The self‐configuring characteristics and dynamic architecture of MANET may lead to frequent changes in topology and resource allocation. This mobility results in frequent connection failures, which increase routing overheads and decrease data transmission dependability. A secured clustering technique is essential to enhance the routing performance and establish reliable routing. In MANET, the cluster mechanism is the prominent way to improve the network lifetime and routing mechanism. The proposed secure cluster routing‐based neuro‐fuzzy logic (SCR‐NFL) establishes a secure cluster mechanism via the use of the Trust value method. It guarantees cluster stability by taking into account node mobility and energy while selecting cluster heads. The important components in the proposed system are Mobility (M), Residual Energy Level (REL), and Trust Value (TV). The neural fuzzy logic system is implemented to handle the data uncertainty and make effective decisions during routing path selections. The proposed SCR‐NFL performance is measured in an NS‐2 simulation environment. The result is compared with the existing Improved Particle Swarm Optimization Algorithm, Trust‐Based Secure Neuro Fuzzy Clustering mechanism and Mobility Aware Energy Efficient Clustering system using Particle Swarm Optimization mechanism. The performance parameters for comparison are packet delivery ratio, average number of clusters, end‐to‐end delay, average number of cluster instances, mobility speed and mobility distance.

Systematic Review of Usability Factors, Models, and Frameworks with Blockchain Integration for Secure Mobile Health (mHealth) Applications

This systematic review examines critical usability factors that influence the adoption of mobile health (mHealth) applications among older adults and identifies gaps in current usability models, including ISO 9241-11, Nielsen’s heuristics, and PACMAD. This review also explores the potential role of blockchain technology in enhancing multimodal medical data systems within mHealth applications. A comprehensive search across six databases yielded 1,073 studies, with 60 meeting inclusion criteria. Studies were analyzed through thematic synthesis to identify key success factors (RQ1) and comparative analysis to assess limitations in existing frameworks (RQ2). Key factors promoting mHealth adoption included ease of use, efficiency, error prevention, learnability, memorability, and user satisfaction. Blockchain integration emerged as a promising approach to improve data security, interoperability, and user trust, particularly for older adults who engage with complex, multimodal health data. Findings from RQ2 highlighted gaps in usability models, such as the lack of age-specific guidance for multimodal interaction, error recovery, and data privacy. These results underscore the need to define a new usability framework and incorporate blockchain to meet the unique needs of older adults in mHealth applications, supporting both secure and accessible healthcare management.

Mobile security awareness of university students in Hong Kong: demographics and education

PurposeThis research investigates mobile security awareness among university students in Hong Kong, who increasingly rely on mobile devices for their daily activities and academic needs. This research seeks to inform targeted educational strategies and policies to enhance mobile security practices among young adults, particularly in regions similar to Hong Kong, where mobile usage is extensively integrated into everyday life.Design/methodology/approachUtilizing an online survey, this research assessed the mobile security awareness of 407 university students from Hong Kong. The Mann-Whitney U-test and other statistical methods were employed to analyze differences in security awareness based on demographic factors such as IT background, gender, educational level and participation in mobile security courses.FindingsThe research revealed a generally high level of mobile security awareness compared to similar research in other regions. It also highlighted that despite no significant difference in awareness between genders, students from IT-related fields or those who participated in mobile security courses exhibit higher awareness levels. These findings underscore the impact of focused education and training on enhancing mobile security awareness.Originality/valueThis research contributes to the limited but growing body of literature on mobile security awareness from the end-user perspective, particularly among university students in the Asia Pacific region. It offers valuable insights for governments, educators and corporate policymakers worldwide, providing a basis for integrating mobile security education into broader academic and professional training programs.

A Scale Development and Application Study on Smartphone Security Awareness

This study aims to assess the extent of smartphone security awareness among students pursuing an associate degree and to ascertain if there are substantial variations in smartphone security awareness based on certain demographic variables. Furthermore, its objective is to uncover the correlation between smartphone security awareness and proficiency in digital literacy. The Smartphone Security Awareness Scale was initially designed with this particular aim. The scale comprises nine items that are measured along a single dimension, accounting for 39.6% of the overall variation. The scale’s reliability coefficient was found to be 0.78. The study sample comprised 612 associate degree students pursuing further education in two vocational colleges. The data were obtained using the internet and voluntarily. The data indicate that associate degree students had a high level of awareness regarding smartphone security. There was no significant difference in smartphone security awareness based on gender, use of security software, participation in information security training, and level of care regarding the privacy and safeguarding of personal data. No significant relationship was found between smartphone security awareness and smartphone ownership duration. However, significant differences were found according to experience of security problems and competence in using information technologies. A positive, significant, and moderate correlation was discovered between smartphone security awareness and proficiency in digital literacy. The scale devised during the investigation can be utilised in other future studies. According to the results obtained in this direction, training and seminars can be organised to increase smartphone security awareness.

AI-Enhanced Secure Mobile Banking System Utilizing Multi-Factor Authentication

The increasing reliance on mobile banking has significantly heightened the need for robust security mechanisms to protect users from unauthorized access and fraudulent activities. As mobile banking continues to grow in popularity, safeguarding financial transactions and personal data becomes a top priority. This paper introduces an AI-enhanced secure mobile banking system that leverages Multi-Phase Authentication (MPA) to strengthen the authentication process. In this system, artificial intelligence is integrated with traditional authentication methods, creating a dynamic framework that assesses the risk level associated with each user interaction. Based on this real-time risk assessment, the system adjusts the authentication requirements, making them more stringent when higher risks are detected and more lenient when the risk is lower. This adaptive mechanism not only enhances the security of mobile banking by providing multiple layers of protection but also improves the user experience by reducing unnecessary authentication steps that can cause frustration and delay. The proposed system's effectiveness is validated through a series of simulations and case studies, which demonstrate significant improvements in key security metrics. These include a marked reduction in instances of fraud and lower false positive rates, which indicate that the system can accurately distinguish between legitimate and suspicious activities without imposing undue burden on users. Overall, the results of this study highlight the potential of AI-enhanced multi-phase authentication to provide a scalable and user-friendly solution for secure mobile banking. This approach represents a promising direction for the future of digital financial services, offering a balance between rigorous security and seamless user experience.

The Feeling of Safety by Pedestrians at Night: An Overlooked Aspect of Climate Change?

As the climate becomes more extreme and heat waves become more prevalent, the effects of climate change spill over into previously unnoticed areas. One such prominent result of global warming is the adverse effect of outdoor weather on pedestrians at night. To investigate this rather overlooked effect, we carried out a large-scale field study in 232 different locations in three different cities in Israel–Tel Aviv-Yafo (106 locations), Haifa (49 locations), and Beersheba (77 locations). The study, involving 30,216 observations on the feeling/s of safety (FoS) performed by 491 participants, started in August 2019 and lasted almost one year. As the study reveals, people feel safer, with all other factors being constant, when the temperature is moderate and humidity is high. According to the study findings, if temperature increases from 25 °C to 30 °C, illumination should be increased by ~20 lx to maintain the same level of FoS. However, if the temperature drops, less illumination can be supplied, which makes a case for smart illumination policies. As providing sufficient FoS is important for an active life outdoors, this study generates knowledge that can help support active and secure mobility in urban areas and beyond. As temperatures rise and humidity patterns change, our findings may have broad implications for urban areas worldwide, both in Israel and beyond.

Implementing Enterprise-Scale iOS MDM : A Technical Deep Dive

As organizations increasingly adopt mobile technologies, the need for effective remote management of iOS devices has grown significantly. This technical article examines implementation strategies and real-world performance metrics from a study of Mobile Device Management (MDM) implementations across 12 diverse organizations, encompassing 2,500 enterprise devices during 2023-2024. The article reveals that optimized MDM solutions can achieve 99.99% uptime while reducing infrastructure costs by up to 45%. Key findings demonstrate that effective MDM implementation reduces device management costs by 64%, improves security compliance rates to 99.3%, and decreases incident response time by 73%. Through automated troubleshooting and optimization, organizations experienced a 67% reduction in security incidents and a 42% decrease in IT support overhead while achieving 85% reduction in deployment time and 82% acceleration in app deployment processes. The article comprehensively evaluates architecture design, deployment metrics, security implementation, performance analysis, and scalability considerations. Implementation of advanced security protocols resulted in a 98.7% prevention rate for common mobile security threats, while maintaining 99.99% system reliability. The article also highlights the importance of phased deployment strategies, which demonstrate 43% higher success rates compared to direct full-scale implementations, and provides strategic insights for future planning and infrastructure optimization.

Usable Privacy and Security in Mobile Applications: Perception of Mobile End Users in Saudi Arabia

Privacy and security is very critical for mobile users and in-depth research into the area highlights a need for more scientific literature on the perception and challenges of end users to better align the design of privacy and security controls with user expectations. In this paper, we have explored the perceptions of the usability of privacy and security settings in mobile applications from mobile users in Saudi Arabia. The findings highlight that gender, age, and education level of users do not have any positive correlation with the privacy and security usability perceptions of mobile users. On the other hand, user concerns about privacy and security and the trustworthiness levels of end users regarding mobile phone privacy and security have a positive impact on end users’ perception of privacy and security usability. Furthermore, privacy usability perception has a positive impact on users’ feelings about their control over the privacy and security of their mobile phones. Based on the results of this empirical study, we propose that user-centric design of privacy and security controls, transparent data handling policies, periodic data management status preview and validation by end users, user education guidelines, strict governmental policies, and automated security settings recommendations can enhance the usability of the privacy and security of mobile phone applications. Our study did not take the geographical location of respondents into account, nor were the respondents balanced based on age and gender. In future work, these weaknesses need to be taken into account, and more qualitative studies can help to extract design guidelines for usable and secure mobile applications.

Modeling Of Mask Detection Systems, Distance Between Objects And Facial Recognition Using The Tiny-YOLOV4 Method, Convolutional Neural Network, And Viola Jones

The development of technology is currently increasing very rapidly, the latest technology such as Face Detection and Face Recognition in public facilities has been widely applied, such as cameras for attendance, facial detection devices as mobile phone security and various other new innovations in the field of technology. The purpose of creating this model is to determine the distance, and the use of masks in public places and to determine the performance when the two algorithms between Tiny-YOLOV4 and Viola Jones are combined. This method uses CPU testing with a clock speed of 2.9 GHz and GPU with a clock speed of 1590 MHz with an accuracy level of 92.6% for mask object detection and 90.67% for face recognition with a maximum distance in front of the camera for human detection is 830 centimeters, mask detection 730 centimeters, and face recognition 530 centimeters. The results of the study produced an accuracy of 92.6% for mask detection and an accuracy of 90.67% for face recognition at maximum distances of 730 cm and 530 cm respectively. The conclusion shows that this system is effective for detecting masks, faces, and distance, providing a significant tool for monitoring compliance in public spaces. Implementing this system can serve as a preventive measure for potential future pandemics

Explanatory and predictive modeling of cybersecurity behaviors using protection motivation theory

ContextProtection motivation theory (PMT) is the most frequently used theory in understanding cyber security behaviors. However, most studies have used a cross-sectional design with symmetrical analysis techniques such as structure equation modeling (SEM) and regression. A data-driven approach, such as predictive modeling, is lacking and can potentially evaluate and validate the predictive power of PMT for cybersecurity behaviors. ObjectiveThe objective of this study is to assess the explanatory and predictive power of PMT for cyber security behaviors related to computers and smartphone. MethodAn online survey was employed to collect data from 1027 participants. The relationship of security behaviors with threat appraisal (severity and vulnerability) and coping appraisal (response efficacy, self-efficacy and response cost) components were tested via explanatory and predictive modeling. Explanatory modeling was employed via SEM, whereas three machine learning algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) were used for predictive modeling. Wrapper feature selection was employed to understand the most important factors of PMT in predictive modeling. ResultsThe results revealed that the threat severity from the threat appraisal component of PMT significantly influenced computer security and smartphone security behaviors. From the coping appraisal, response efficacy and self-efficacy significantly influenced computer and smartphone security behaviors. The ML analysis showed that the highest predictive power of PMT for computer security was 76 % and for smartphone security 68 % by KNN algorithm. The wrapper feature selection approach revealed that the most important features in predicting security behaviors are self-efficacy, response efficacy and intention to secure devices. Thus, the findings indicate the complementarity of the cross-sectional and data driven methods.

Examining the Awareness of Mobile Money Users on Security Risks in Tanzania to Enhance Cybersecurity Literacy: A Case Study of Airtel Money

The proliferation of mobile money services in Tanzania has revolutionized financial inclusion, but it has also exposed users to increasing cyber threats. This study examines the level of cybersecurity awareness among mobile money users, focusing on Airtel as a case study, to identify gaps and opportunities for enhancing cybersecurity literacy. The research explores users’ exposure to formal training, the impact of Airtel’s educational materials, and users’ confidence in securing their accounts. Findings reveal a significant lack of formal training, with most users relying on Airtel’s security tips to develop cybersecurity knowledge. While these resources have improved user confidence, there is a demonstrated demand for structured workshops and comprehensive training programs. The study underscores the need for collaborative efforts between service providers, policymakers, and educational institutions to address these gaps. Enhancing cybersecurity literacy through accessible, interactive, and user-centred approaches is essential to fostering a secure mobile money ecosystem in Tanzania.

Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors.

Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone's usage behavior, while the owner's finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner's finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of to , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.

Utilizing AspectJ for Defense Against Evasive Malware Attacks in Android System

Introduction: Mobile devices have become an integral part of our digital lives, facilitating various tasks and storing a treasure trove of sensitive information. However, as more people utilize mobile devices, sophisticated cyber threats are emerging to elude traditional security measures. Method: The use of evasion techniques by malicious actors presents a significant challenge to mobile security, necessitating creative solutions. In this work, we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics, including runtime manipulation, code obfuscation, and unauthorized data access. Results: These tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues, this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor, intercept, and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape. Conclusion: Consequently, this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.

Android Malware Detection Using Support Vector Regression for Dynamic Feature Analysis

Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to develop a more accurate and reliable malware detection system capable of identifying both known and novel malware variants. We implemented a comprehensive methodology encompassing dynamic feature extraction from Android applications, feature preprocessing and normalization, and the application of SVR with a Radial Basis Function (RBF) kernel for malware classification. Our results demonstrate the SVR-based model’s superior performance, achieving 95.74% accuracy, 94.76% precision, 98.06% recall, and a 96.38% F1-score, outperforming benchmark algorithms including SVM, Random Forest, and CNN. The model exhibited excellent discriminative ability with an Area Under the Curve (AUC) of 0.98 in ROC analysis. The proposed model’s capacity to capture complex, non-linear relationships in the feature space significantly enhanced its effectiveness in distinguishing between benign and malicious applications. This research provides a robust foundation for advancing Android malware detection systems, offering valuable insights for researchers and security practitioners in addressing evolving malware challenges.

Mobile Device Security: A Two-Layered Approach with Blockchain and Sensor Technology for Theft Prevention

In the backdrop of the escalating incidents of mobile device theft and associated security challenges, a resilient and innovative solution is imperative. The traditional security mechanisms, largely reliant on the International Mobile Equipment Identity (IMEI), have been fraught with vulnerabilities, leading to a surge in incidents of device theft and data breaches. Addressing this pressing issue, we present a novel, two-tiered approach integrating sensor technology and blockchain to bolster mobile device security. This work aims to create and assess a dual-layered security strategy that uses blockchain and sensor technologies in a complementary way. Based on a rigorous conceptual framework, it explores a two-tiered security model that intricately combines sensor and blockchain technologies. This collaborative integration aims to provide an effective solution to the widespread challenges of theft and security breaches in mobile devices. The methodology employs a sensor layer for real-time data collection and processing to detect potential thefts, and activating alerts. The blockchain layer, invoked upon these alerts, initiates secure, transparent, and decentralized transactions for verification and validation across network nodes. This dual mechanism ensures swift and secure anti-theft actions, supported by an enhanced encryption standard. Our result analysis reveals the proposed system’s superiority in computational time, energy consumption, and overall security levels when compared to existing protocols. The integration of real-time processing and blockchain’s immutable nature promises reduced false positives and enhanced data integrity. The findings indicate that this integrative approach not only mitigates theft but also ensures data security, marking a significant stride in mobile security technology. In conclusion, this two-layered system promises a scalable, efficient, and robust solution to mobile device theft and data breaches, with potential impacts transcending individual device security to influence broader data privacy and security paradigms, thus signifying a pivotal development in the field of mobile security.