Mobile Device Security Research Articles

A Survey on Mobile Digital Forensic: Taxonomy, Tools, and Challenges

ABSTRACTModern time largely rely on mobile gadgets as a means of communication, and entertainment. These gadgets now plays a vital role in forensic analysis and criminal investigations as it store a plethora of private and sensitive data. Investigating feasible digital incidents and preserving mobile device security became crucial as the proliferation of mobile devices continues to influence modern society. In this survey; mobile threats, investigative process models for mobile forensics, evidence sources, forensics tools, and field problems are all covered in‐depth. Further, a comprehensive analysis examining the capabilities, features, and efficiency of available distinct mobile forensic tools namely Belkasoft, MOBILedit, and Magnet AXIOM for retrieving, analyzing, and presenting digital evidence is being presented. The objective of the study is to assist forensic investigators in choosing the best tool for specific investigations. The findings of our study reveals the complexity of mobile operating systems, investigative process supported with sources of evidence, and forensic tools which makes it clear that the area of mobile forensics is dynamic and require modern skill adaptation along and innovative development in future. Finally, the study presents current challenges encountered during mobile forensic investigations as researchers can enhance their procedures and tactics for mobile device examination and evidence extraction by fostering a greater knowledge of the difficulties involved in mobile forensics.

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.

Delusio - Plausible Deniability For Face Recognition

We developed an Android phone unlock mechanism utilizing facial recognition and specific mimics to access a specially secured portion of the device, designed for plausible deniability. The widespread adoption of biometric authentication methods, such as fingerprint and facial recognition, has revolutionized mobile device security, offering enhanced protection against shoulder-surfing attacks and improving user convenience compared to traditional passwords. However, a downside is the potential for third-party coercion to unlock the device. While text-based authentication allows users to reveal a hidden system by entering a special password, this is challenging with face authentication. We evaluated our approach in a role-playing user study involving 50 participants, with one participant acting as the attacker and the other as the suspect. Suspects successfully accessed the secured area, mostly without detection. They further expressed interest in this feature on their personal phones. We also discuss open challenges and opportunities in implementing such authentication mechanisms.

Solutions for Mobile Devices Security Concerns, Aden Refinery Company (A.R.C) Case Study

Companies that allow their employees to use mobile devices for work purpose, they suffer from cybersecurity challenges such as virus or malware, download illegal content, poor authorization and authentication, etc. These challenges need solutions or controls to solve or mitigate them. In this paper, there are two objectives. The first aim is to know which of the security controls were asked in the questionnaire used in the A.R.C. The second objective is to know which security scenarios the IT department uses to protect the information from potential mobile device security concerns. A descriptive methodology was employed in this study. Data were collected from forty people in the IT department at Aden Refinery Company through a questionnaire. The results show that some security controls, such as antivirus/ antimalware, backups, firewalls, and policies, are used in the A.R.C.

Unlocking Security: The Significant Role of Machine Learning and Deep Learning Techniques in Fingerprint Recognition Systems

One of the more widely utilized biometrics for in-person identification is a fingerprint. It has been discovered that no two people have the same fingerprints and that each one is distinct. A person's fingerprint traits remain constant as they age. Compared to DNA, fingerprints are more distinctive. Identical twins cannot have the same fingerprints even though they have the same DNA. Artificial Intelligence encompasses the broader goal of creating intelligent systems, while Machine Learning focuses on developing algorithms that enable machines to learn from data. Deep Learning, as a subset of Machine Learning, leverages neural networks with multiple layers to learn complex representations of data. While Deep Learning has become increasingly prominent and successful in recent years, it is just one of the many approaches within the broader field of AI and Machine Learning. Fingerprint recognition stands as one of the oldest and most widely used biometric authentication methods, finding applications across diverse domains such as law enforcement, access control, and mobile device security. With the advent of machine learning and deep learning techniques, significant strides have been made in enhancing the accuracy, efficiency, and scalability of fingerprint recognition systems. This comprehensive review aims to provide a thorough examination of the pivotal role played by machine learning and deep learning methodologies in advancing fingerprint recognition technology. The paper commences with an introduction to the importance and ubiquity of fingerprint recognition, elucidating its significance in various real-world applications. Subsequently, it delves into the foundational concepts of machine learning and deep learning, elucidating their relevance to fingerprint recognition tasks. Key machine learning algorithms such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Random Forests, and neural network-based approaches are discussed, highlighting their strengths and limitations in the context of fingerprint recognition.

A Model for Detection and Prevention of AndroRat in Mobile Devices

Android operating systems has gained increasing popularity in smart phones and other mobile intelligent terminals in recent years. Unpleasantly, the accumulation development and open nature of the platform has also attracted a vast number of malware developers. This dissertation presents a model for detecting and preventing AndroRat, a notorious Remote Administration Tool (RAT), on android devices. The research systematically pursued predefined objectives, leading to the development of a robust detection framework that utilizes the Outlier Technique. This framework significantly enhances the accuracy of AndroRat identification. Moreover, the dissertation introduces a preventive mechanism based on Recurrent Neural Network (RNN), fortifying Android phones against potential threats posed by RATs. By achieving the outlined objectives, this research makes a noteworthy contribution to the field of mobile device security. It demonstrates a practical application of machine learning techniques in mitigating evolving cybersecurity threats. A dataset was gathered for constructing the model. Object Oriented Analysis and Design (OOAD) was used as the research methodology and python was used as the programming language. The implementation of the Python Programming Language in this system not only ensured efficient execution but also established a versatile and accessible platform, laying the groundwork for future enhancements and adaptations. The comparative analysis conducted against existing systems highlights the effectiveness and innovation embedded in the proposed model, affirming its potential as a valuable addition to the realm of AndroRat detection and prevention with an accuracy of 99.99%. This dissertation not only addresses current security challenges but also establishes a foundation for continued advancements in mobile device security through the integration of cutting-edge technologies.

Implementation on Anti-Theft Mobile Tracking and Monitoring System

The rapid proliferation of mobile devices has led to a significant increase in the cases of mobile theft and unauthorized access to personal data. To address these security concerns, an “Antitheft Mobile Tracking and Monitoring System” is proposed. The application aims to enhance the security of mobile devices by providing users with the means to track and recover their stolen or lost devices. The Antitheft Mobile Tracking and Monitoring System offers a comprehensive solution to combat mobile theft and enhance device security. By combining real-time GPS tracking, image capturing and use of mail protocols to facilitate them has provided it with capabilities with strong authentication measures, the system provides users with effective tools to mitigate the risks associated with mobile device theft. As mobile devices continue to play an integral role in modern life, such security measures are essential to protect user privacy and personal information. The widespread adoption of mobile devices has revolutionized the way we communicate, work, and access information. With this technological advancement, however, comes an unfortunate increase in the incidents of mobile theft and unauthorized access to personal data. The loss of a mobile device not only represents a financial setback but also exposes users to potential breaches of their privacy and security. This real-time tracking capability enables users and authorized personnel, such as law enforcement agencies, to pinpoint the device's location accurately. Key Words: GPS technology, Real-time Notification, SMTP, Authentication.

A Comparative Analysis of Malware Detection Methods Traditional vs. Machine Learning

Mobile devices have been the target of malicious software since their beginnings. Two known types
 of malware threats can intrude into mobile, independently injected applications and fraudulent
 applications that are developed to breach the security of mobile devices. Mostly these fraudulent
 applications access data using API calls and permission requests. API calls and permission requests
 are important for smooth conversation between mobile devices and database servers. This research
 proposes an efficient classification model that concatenates API calls and permission requests to
 detect malicious applications. We have used a dataset that contained more than 15 thousand Android
 devices’ malware. We have divided data into three groups to differentiate between the malicious
 permission requests and malicious API calls with normal permission requests and normal API calls.
 To increase the probability of recognizing Android malware applications, we develop three distinct
 grouping strategies for selecting the most valuable API calls that are obscure, critical, and obstreperous and are chosen because Android apps extensively use several application programming interfaces (APIs). According to the results, malware applications require authorizations to access confidential information very frequently than normal Android applications do. Also, malicious Android
 applications raise a diverse set of API calls to access sensitive data, evidenced by malware applications making a distinct set of API calls. Our proposed method attains an F-score of 94.04%, which suggests that it is efficient at discovering mobile malware applications. Our model can be of significant assistance in conducting mobile application analysis and forensic investigations into malware.

A Framework for Security Assessment of Android Mobile Banking Applications

Mobile banking applications make users' daily lives easier by allowing them to access banking services, such as balance inquiries and bill payments, anytime and anywhere. Since these applications manage very sensitive financial data, special attention must be paid to data security. Several works in the literature assess the security of mobile banking applications. However, we observe the lack of a widely adopted framework among researchers for assessing the security of mobile banking applications. In this paper, we propose a framework consisting of twenty-six criteria for assessing the security of Android mobile banking applications. These criteria are divided into five categories: mobile device security, data in transit, data storage, cryptographic misuse, and others. Subsequently, we evaluate the proposed framework based on predefined requirements. These requirements are no redundancy, no ambiguity, and comprehensiveness. As a case study, we assess the security of the Android mobile banking applications of seven major Canadian banks. The results show that data in transit is adequately protected by these applications.

Parallel-pipelined-memory Blowfish FPGA-based radio system with improved power-throughput for secured IoT network

The Internet of Things (IoT) has emerged as a disruptive force, transforming industries, cities, and daily life in a time of unprecedented connection. This outcome has called for robust security, reliability, and efficient power management for the IoT network. The security of small mobile devices within the IoT network has become a pressing concern due to the multitude of wireless vulnerabilities that threaten data transmission, the harsh wireless environment for data transmission, and the need for low-power consumption devices. This study proposed the development of a new parallel-pipelined-memory (P2M) Blowfish field programmable gate array (FPGA)-based radio system as a prototype of a secured mobile device for IoT application. The FPGA platform verified the proposed system, indicating a minimum of 64 % power-throughput improvement. In this case, the hardware utilisation was reduced by 3.5 % compared to the recently published works. Meanwhile, the real-time transmission analysis of the suggested P2M Blowfish radio system in a non-line-of-sight (NLOS) indoor environment indicated that the transmitted data over 2.4 GHz ZigBee standard at 10 dBm radio frequency (RF) power level demonstrated the optimum signal quality with received signal strength and signal-to-noise ratio (SNR) of −34.58 dBm and 39.06 dB, respectively. This feature also included a 6.25 × 10−3 minimum bit error rate (BER) at 61 m as the maximum communication range. Thus, the P2M Blowfish FPGA-based radio system could improve the security, reliability, and power efficiency of IoT networks.

A malware detection model based on imbalanced heterogeneous graph embeddings

The proliferation of malware in recent years has posed a significant threat to the security of computers and mobile devices. Detecting malware, especially on the Android platform, has become a growing concern for researchers and the software industry. This paper proposes a new method for detecting Android malware based on unbalanced heterogeneous graph embedding. First of all, most malware datasets contain an imbalance of malicious and benign samples, since some types of malware are scarce and difficult to collect. Thus, as a result of this problem, the classification algorithm is unable to analyze the minority samples through sufficient data, resulting in poor downstream classifier performance, in light of the fact that adversarial generation networks possess the characteristic of completing data, an algorithm for generating graph structure data is presented, in which nodes are generated to simulate the distribution of minority nodes within a network topology. Then, considering that heterogeneous information networks have the characteristics of retaining rich node semantic features and mining implicit relationships, heterogeneous graphs are used to construct models for different types of entities (i.e. Apps, APIs, permissions, intents, etc.) and different meta-paths. Finally, a new method is introduced to alleviate the over-smoothing phenomenon of node information in the propagation of deep network. In the deep GCN, we first sample the leader nodes of each layer node, and then add a residual connection and an identity map in order to determine the characteristics of the high-order leader. In this paper, a self-attention-based semantic fusion method is also applied to adaptively fuse embedded representations of software nodes under different meta-paths. The test results demonstrate that the proposed IHODroid model effectively detects malicious software. In the DREBIN dataset, which consists of 123,453 Android applications and 5,560 malicious samples, the IHODroid model achieves an accuracy of 0.9360 and an F1 score of 0.9360, outperforming other state-of-the-art baseline methods.

A Novel Approach to Android Malware Intrusion Detection Using Zero-Shot Learning GANs

This study proposes an innovative intrusion detection system for Android malware based on a zero-shot learning GAN approach. Our system achieved an accuracy of 99.99%, indicating that this approach can be highly effective for identifying intrusion events. The proposed approach is particularly valuable for analyzing complex datasets such as those involving Android malware. The results of this study demonstrate the potential of this method for improving the accuracy and efficiency of intrusion detection systems in real-world scenarios. Future work could involve exploring alternative feature selection techniques and evaluating the performance of other machine learning classifiers on larger datasets to further enhance the accuracy of intrusion detection systems. The study highlights the importance of adopting advanced machine learning techniques such as zero-shot learning GANs to enhance the effectiveness of intrusion detection systems in cybersecurity. The proposed system presents a significant contribution to the field of intrusion detection, providing an effective solution for detecting malicious activities in Android malware, which can improve the security of mobile devices.

Enhancing BYOD mobile device security in a hybrid environment

Research on enhancing Bring Your Own Device (BYOD) security in hybrid environments is critically relevant and important to protect data and ensure customer and organizational trust through the development of strong security policies. The study aims to develop and implement effective strategies and interventions that will help ensure that corporate data and resources are protected when employees use personal mobile devices in an environment where both on-premises and cloud-based resources are present. The methods used include analytical, deduction, modeling, statistical, and synthesis methods. This research identified specific vulnerabilities of potential threats to BYOD mobile devices in a hybrid environment and developed security strategies that include recommendations for creating a security policy and using a mobile device management system. The research led to the development of specific technical solutions, such as recommendations for mobile device configuration. This included educating employees and managers about security rules and the importance of adhering to security policies, and updated lists of current BYOD security threats and challenges given the changing threat environment. The practical value of the work lies in providing practical relevance to organizations by providing robust protection of sensitive data and resources, increasing employee comfort and confidence in the security of personal devices, easing IT departments’ tasks of device management and threat detection, and increasing customer and partner confidence in a security-layer organization.

A Survey on Anti-Theft Mobile Tracking and Monitoring System

The rapid proliferation of mobile devices has led to a significant increase in the cases of mobile theft and unauthorized access to personal data. To address these security concerns, an “Antitheft Mobile Tracking and Monitoring System” is proposed. The application aims to enhance the security of mobile devices by providing users with the means to track and recover their stolen or lost devices. The Antitheft Mobile Tracking and Monitoring System offers a comprehensive solution to combat mobile theft and enhance device security. By combining real-time GPS tracking, image capturing and use of mail protocols to facilitate them has provided it with capabilities with strong authentication measures, the system provides users with effective tools to mitigate the risks associated with mobile device theft. As mobile devices continue to play an integral role in modern life, such security measures are essential to protect user privacy and personal information. The widespread adoption of mobile devices has revolutionized the way we communicate, work, and access information. With this technological advancement, however, comes an unfortunate increase in the incidents of mobile theft and unauthorized access to personal data. The loss of a mobile device not only represents a financial setback but also exposes users to potential breaches of their privacy and security. This real-time tracking capability enables users and authorized personnel, such as law enforcement agencies, to pinpoint the device's location accurately.

Mobile Botnet Detection A Machine Learning Approach using SVM

The security and privacy of smartphone users are seriously threatened by mobile botnets, which allow malevolent actors to carry out a variety of illegal actions, such as DDoS attacks, data theft, and resource exploitation. These mobile botnets are becoming more and more sophisticated, making it difficult to detect them with conventional signature-based and heuristic methods. This study proposes a machine learning-based method for mobile botnet detection that makes use of Support Vector Machines (SVM).The study focuses on behavioural feature extraction from mobile device system-level data and network traffic. The SVM model is used for classification once feature selection techniques have been used to select the most pertinent and discriminative attributes. The SVM model, making use of its capacity to manage nonlinear classification and high-dimensional data.Tests carried out on a variety of network traffic and system behaviour datasets gathered from mobile devices show encouraging outcomes for the detection of botnets. Compared to conventional detection techniques, the SVM classifier outperforms them in identifying mobile botnet activities with a high degree of accuracy, precision, and recall.The suggested SVM-based method improves mobile device security by offering a flexible and successful mobile botnet detection solution. The results of this study open the door to the development of strong and durable mobile security systems by providing insights into the proactive identification and mitigation of mobile botnet threats through the use of machine learning techniques

Soft Smart Biopatch for Continuous Authentication‐Enabled Cardiac Biometric Systems

AbstractBiometric locking systems offer a seamless integration of an individual's physiological characteristics with secure authentication. However, they suffer from limitations such as false positive and negative authentication, environmental interference, and varying disadvantages across multiple authentication methods. To address these limitations, this study develops a soft smart biopatch for a continuous cardiac biometric wearable device that can continuously gather novel biometric data from an individual's heart sound for authentication with minimal error (less than 0.5%). The device is designed to be discreet and user‐friendly, and it employs soft biocompatible materials to ensure comfort and ease of use. The patch system incorporates a miniaturized microphone to monitor sounds over long periods and multiple dimensions, enhancing the reliability of the biometric data. Furthermore, the use of machine‐learning algorithms has enabled the creation of unique identification keys for individuals based on the continuous monitoring properties of the low‐cost device. These advantages make it more effective and efficient than traditional biometric systems, with the potential to enhance the security of mobile devices and door locks.

An analysis of differences in behaviors and practices of security-conscious users and regular users on mobile devices

PurposeThis study aims to investigate the differences in security-conscious (group A) and regular (group B) users’ behaviors and practices on mobile devices.Design/methodology/approachA survey was used to investigate the differences in behaviors and practices of security-conscious users (group A) and regular users (group B) on mobile devices. Each group will have 50 participants for a total of 100.FindingsThe analysis revealed differences in the behaviors and practices of security-conscious and regular users. The results indicated that security-conscious users engage in behaviors and practices that are more secure on mobile devices when compared with regular users.Research limitations/implicationsThe results will help recommend the best behaviors and practices for mobile device users, increasing mobile device security.Social implicationsThe results will help society to be more aware of security behaviors and practices on mobile devices.Originality/valueThis study answers the call for addressing the weaknesses and vulnerabilities in mobile device security. It develops a research instrument to measure the differences in behaviors and practices of security-conscious and regular mobile device users.

Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Throughout the past several decades, mobile devices have evolved in capability and popularity at growing rates while improvement in security has fallen behind. As smartphones now hold mass quantities of sensitive information from millions of people around the world, addressing this gap in security is crucial. Recently, researchers have experimented with behavioral and physiological biometrics-based authentication to improve mobile device security. Continuing the previous work in this field, this study identifies popular dynamics in behavioral and physiological smartphone authentication and aims to provide a comprehensive review of their performance with various deep learning and machine learning algorithms. We found that utilizing hybrid schemes with deep learning features and deep learning/machine learning classification can improve authentication performance. Throughout this paper, the benefits, limitations, and recommendations for future work will be discussed.

Sniffer for Detecting Lost Mobiles

The main goal of this Endeavour is to use sniffer technology to track down a misplaced cell phone. Regularly thousands of adaptable get misplaced. Security for mobile devices is necessary since portable companies are now building mobile devices with IMEI to locate the mobile device, but this system is not very effective. Hence, sniffer technology is applied. This device, which combines a Sniffer Base Station, an Unidirectional Antenna, and Tracking Software, can be referred to as a Base Station. To maintain the recurrence, the Sniffer Base Station is used. Recurrence is a VHF extension at 900 MHzTo exchange the flag relating to the desired recurrence for receiving and transmitting signals, unidirectional radio wire is employed. The most important component of cell phone monitoring is the programming. Because of the way this programming is laid out, the IMEI of the device is read from its Memory. It checks for a signal from a device that responds to a flag sent by sniffer technology. The IMEI number, which stands for “International Mobile Station Equipment Identity,” identifies a device that uses GSM technology.

Apple in My Eyes (AIME): Liveness Detection for Mobile Security Using Corneal Specular Reflections

The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and presentation attacks (PAs). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user’s physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3-D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3-D face spoofing. This article presents a software-based facial liveness detection method named Apple in My Eyes (AIME). AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweight machine learning techniques. AIME system components include challenge and pattern detection, feature extraction and classification, and data augmentation and training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the Web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200-ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users’ active responses.