ABSTRACT
An automated reminder mechanism is built in this Android-based application. It emphasizes the contact between doctors and patients. Patients can set a reminder to remind them when it is time to take their medicine. Multiple medications and timings, including date, time, and medicine description, can be programmed into the reminder by using image processing. Patients will be notified through a message within the system, as preferred by the patients. They have the option of looking for a doctor for assistance. In this COVID-19 pandemic situation where nurses have to remind the patients in the hospitals to take their medications, our application can be useful, alerting the patient every time of the day when he/she has to take the medicine and in what amounts. Also, all the necessary tests report and prescriptions can be saved on the cloud for later use. Patients will be provided with doctor contact information based on their availability. Also, patients will be notified of the expiry date of the medicine, and the former history of the medicines can be stored for further reference. The proposed system prioritizes a good user interface and easy navigation. Image processing will be accurate and efficient with the help of powerful CNN-RNN-CTC algorithm. It also emphasizes on a secure storage of the user's data with the help of the RSA algorithm for encryption and the gravitational search algorithm for secure cloud access. We attempted to create a Medical Reminder System that is cost-effective, time-saving, and promotes medication adherence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
One of many challenges created by COVID-19 pandemic is to reduce need of contact. Quick Response (QR) codes offered a readily available solution to this challenge with offer to support contact less processes. Wide adaption of smart mobile devices like smart phones and tablets and huge number of mobile applications available in the respective application stores, which support QR code scanning acted as a catalyst in rapid adaption of QR codes to support contact less processes. Support of QR code-based processing rapidly increased during the pandemic, penetrated all processes like sales and marketing, authentication, and digital payments to name some. On one hand, this served the cause in terms of reducing contact, on other hand, factors like rapid adaption and using it in smart mobile devices, which are existing to cater to the larger purpose of human usage, scanning QR codes was not in that list to start with is bringing in the series of security issues which can arise starting from the human factor, software, misuse and hacking factors. This paper focuses on the QR code processes, differences in terms of security while using a smart device for QR codes when compared to the rugged device-based barcode scanners, the kind of security issues such process can encounter while using smart devises for QR code scanning, factors that must be considered by the applications development as well as the consumers of such functionality and the way to ensure security of consumers of such functionality. © 2022 IEEE.
ABSTRACT
The malicious actors continuously produce malicious Android applications with a COVID-19 theme in the context of the pandemic. Users frequently grant the necessary permissions to install those phoney apps without paying much attention. Android permissions are essential points of weakness. Major privacy issues often result from this vulnerability. Hackers with malicious intent have viewed the COVID-19 pandemic as an opportunity to conduct malware attacks to profit financially and advance their nefarious goals. Through COVID-19-related content, people are becoming victims of phishing scams. The android malware seen explicitly during the pandemic of Covid-19 is discussed in this study, and we next analyze malware detection methods with a focus on these Covid-19-Themed malware mobile applications. This research paper attempts to identify dangerous android permissions and the malware families that erupted during the Covid-19 outbreak. © 2023 IEEE.
ABSTRACT
Many religious communities are going online to save costs and reach a large audience to spread their religious beliefs. Since the COVID-19 pandemic, such online transitions have accelerated, primarily to maintain the existence and continuity of religious communities. However, online religious services (e.g., websites and mobile apps) open the door to privacy and security issues that result from tracking and leakage of personal/sensitive information. While web privacy in popular sites (e.g., commercial and social media sites) is widely studied, privacy and security issues of religious online services have not been systematically studied. In this paper, we perform privacy and security measurements in religious websites and Android apps: 62,373 unique websites and 1454 Android apps, pertaining to major religions (e.g., Christianity, Buddhism, Islam, Hinduism). We identified the use of commercial trackers on religious websites—e.g., 32% of religious websites and 78% of religious Android apps host Google trackers. Session replay services (FullStory, Yandex, Inspectlet, Lucky Orange) on 198 religious sites sent sensitive information to third parties. Religious sites (14) and apps (7) sent sensitive information in clear text. Besides privacy issues, we also identify sites with potential security issues: 19 religious sites were vulnerable to various security issues;and 69 religious websites and 29 Android apps were flagged by VirusTotal as malicious. We hope our findings will raise awareness of privacy and security issues in online religious services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Mobile Cloud Computing (MCC) also known as on-demand computing uses cloud computing to deliver applications to mobile devices. This new computational paradigm model which plays a big part in the Internet of Things (IoT), has increased its popularity even more during Covid-19 pandemic and became a necessity when schools, businesses and hospitals must work remotely. We can access and process remote data which are stored over the cloud server in real-time by connecting to a wireless network. For accessing any cloud server, a mutual authentication and key agreement between a mobile user and a cloud server provider is required. However, existing authentication schemes for MCC fail to provide user anonymity, server anonymity and user untraceability. Therefore, we propose a Lightweight Authentication Scheme with User Anonymity (LASUA) which artfully employs Elliptic Curve Cryptography (ECC), random number, time stamps, one-way hash functions, concatenation, XOR operations and fuzzy extractor for biometric to enable various security features including anonymity and resistance against various attacks. LASUA utilises the hardness of ECC to provide top-notch security with low computation and communication cost, a perfect solution for resource constrained devices. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has changed many aspects of human life during last three years. One of these aspects is the adaption of new trends and technologies for everyday activities such as delivery and transportation. People now prefer to shop online and get their products delivered at home without wasting any time. Therefore, the security and importance of online and delivery applications is the main concern these days. The payment mode of these applications is online which involves personal data like bank information and user details. This problem led to the research contribution of our work. The main objective and implication of this study is to find personally identifiable information (PII) of users which uniquely identifies a person at personal and organizational scopes. In this paper, we present the forensics analysis of eight popular Android delivery and transport applications i.e. Daraz.pk, Foodpanda, Grocer app, Airlift express, Bykea, Indriver, Uber and Clicky shopping app. These applications have not been previously studied and investigated by other researchers. Furthermore, these applications are among the top android apps used by customers. It is expected that such an analysis can guide investigators towards obtaining useful information about a suspect who has used such an application on their device. The analysis process started with the installation of each application on a rooted Samsung S7 Edge smartphone. Then various activities were performed such as setting up an account, booking a ride, or ordering a delivery. After this, a physical image of the device was acquired. A detailed analysis of the image was carried out using Autopsy and all relevant artifacts were collected. A comparison of the results showed largest number of artifacts have been gathered from installation activity and the most number of unique artifacts have been collected from order and booking activity. A tabular form of analysis has also been shown with all of the source and path files from which the data has been gathered. © 2022 IEEE.
ABSTRACT
Covid 19 has dramatically changed people's lives around the world. It has shut down schools, companies and workplaces, forcing individuals to stay at home and comply to quarantine orders. Thus, individuals have resorted to the Internet as a means for communicating and sharing information in different domains. Unfortunately, some communities are still unserved by commercial service providers. Mobile Adhoc Network (MANET) can be used to fill this gap. One of the core issues in MANET is the authentication of the participating nodes. This mechanism is a fundamental requirement for implementing access control to network resources by confirming a user's identity. In recent years, security experts worldwide proposed distributed authentication for MANET due to the lack of a central authority to register and authenticate nodes. In this article, decentralized authentication based on the technology of fog computing and the concept of the blockchain is proposed. The evaluation of this mechanism satisfies the diverse security requirements and strongly protects the networks from attacks. © 2022 Scientific Association for Infocommunications. All rights reserved.
ABSTRACT
Several organizations have used remote healthcare monitoring solutions using Mobile phone technologies either as Android and or iOS. Such mobile applications are much cheaper to keep the elderly and people who have chronic and infectious diseases (such as pneumonia, COVID-19, SARS, Chest diseases, diabetes and others) in their own homes rather than in health-care facilities. The purpose of this project is to combine state wide detailed health data and mobile data using Flutter Mobile technology. The proposed project will mainly include an analytics component combining health factors and social network analysis to develop health monitoring tool for chronic diseases such as diabetes. It should be noted that a development of a remote monitoring prototype is described and called a Secure Health Monitoring System for Diabetes (SHMSD). Nowadays smartphones such as Android, IOS, Micro Windows and Huawei mobile phones are programmable and include a collection of cheap powerful embedded sensors (such as an accelerometer digital compass gyroscope GPS microphone and camera) which are enabling the emergence of personal and community scale sensing applications. Therefore the sensor-equipped mobile phones are revolutionize the healthcare and social networks sectors. The proposed system combines a Geographic Positioning Systems (GPS) with the ability to remotely access the information from anywhere. Security issues related to such monitoring are also discussed. © 2022 IEEE.
ABSTRACT
Mental health is an extremely important subject, especially in these unprecedented times of the COVID-19 pandemic. Ubiquitous mobile phones can equip users to supplement psychiatric treatment and manage their mental health. Mobile Mental Health (MMH) apps emerge as an effective alternative to assist with a broad range of psychological disorders filling the much-needed patient-provider accessibility gap. However, it also raises significant concerns with sensitive information leakage. The absence of a transparent privacy policy and lack of user awareness may pose a significant threat to undermining the applicability of such tools. We conducted a multifold study of - 1) Privacy policies (Manually and with Polisis, an automated framework to evaluate privacy policies);2) App permissions;3) Static Analysis for inherent security issues;4) Dynamic Analysis for threat surface and vulnerabilities detection, and 5) Traffic Analysis. Our results indicate that apps' exploitable flaws, dangerous permissions, and insecure data handling pose a potential threat to the users' privacy and security. The Dynamic analysis identified 145 vulnerabilities in 20 top-rated MMH apps where attackers and malicious apps can access sensitive information. 45% of MMH apps use a unique identifier, Hardware Id, which can link a unique id to a particular user and probe users' mental health. Traffic analysis shows that sensitive mental health data can be leaked through insecure data transmission. MMH apps need better scrutiny and regulation for more widespread usage to meet the increasing need for mental health care without being intrusive to the already vulnerable population. © 2022 IEEE.
ABSTRACT
Android phones are one of the most common accessories used all over the world. Although once a luxury, it has now become a basic need for all generations. It is a multipurpose tool that can be used for all sorts of necessities and entertainment. Through our android app corona care, a mobile phone can be a helping hand for health care. This app can help prevent the deadly virus known as COVID-19 through plasma donation, consultation with doctors, setting up appointments, predicting corona risk assessment from symptoms using the Gaussian Naive Bayes method of predicting the risk percentage, providing emergency health services and updating users about the safety instructions about Covid-19. Our application consists of most features needed in a mHealth application that can provide necessary medical assistance to each and every household. © 2021 IEEE.
ABSTRACT
Cyber security is the implementation of smart technologies to safeguard computer systems, mobile devices, communication networks or most importantly the sensitive and confidential data saved in those systems or devices from various types of cyber-attacks, unauthorized access, hackers or intruders. Cyber security can also be considered as a subset of information security because information security is a general term. It aims to protect a wider domain which includes all kinds of information assets either hard copy or soft copy. The recent accelerating rise in digitalization due to Covid-19 has brought in many new challenges. The amount of personal data present on the web due to the same has raised concerns among users. However, it's not only the personal data that is a matter of concern but also the dataset which is given as input to numerous machine learning and deep learning models. Local networks are prone to attacks and intrusion activities now more than ever. As a result, cyber security experts have been working on the development of more complex monitoring systems and algorithms for the detection and prevention of such activities. Various technologies like machine learning and deep learning might play a significant role in improving cyber security. It can help in analyzing patterns and improving the models for recognizing similar attacks in future. This research work aims to study intrusion detection systems in detail and differentiate between intrusion detection systems, intrusion prevention systems and firewalls as IDS and IPS are commonly regarded as the same thing. It also highlights the previous works related to this subject along with their suggested methods. © 2022 IEEE.
ABSTRACT
Smartphone has become the 4th basic necessity of human being after Food, Cloths and Home. It has become an integral part of the life that most of the business and office work can be operated by mobile phone and the demand for online classes demand for all class of students have become a compulsion without any alternate due to the COVID-19 pandemic. Android is considered as the most prevailing and used operating system for the mobile phone on this planet and for the same reason it is the most targeted mobile operating system by the hackers. Android malware has been increasing every quarter and every year. An android malware is installed and executed on the smartphones quietly without any indication and user’s acceptance, that possess threats to the consumer’s personal and/or classified information stored. To address these threats, varieties of techniques have been proposed by the researchers like Static, Dynamic and Hybrid. In this paper a systematic review has been carried out on the relevant studies from 2017 to 2020. Assessment of the malware detection capabilities of various techniques used by different researchers has been carried out with comparison of the performance of different machine learning models for the detection of android malwares by assessing the results of empirical evidences such as datasets, features, tools, etc. However the android malware detection still faces several challenges and the possible solution with some novel approach or technique to improve the detection capabilities is discussed in the discussion and conclusion. © 2022, Springer Nature Switzerland AG.
ABSTRACT
In the severe COVID-19 environment, encrypted mobile malware is increasingly threatening personal privacy, especially those targeting on Android platform. Existing methods mainly focus on extracting features from Android Malware (DroidMal) by reversing the binary samples, which is sensitive to the deduction of the available samples. Thus, they fail to tackle the insufficiency of the novel DoridMal. Therefore, it is necessary to investigate an effective solution to classify large-scale DroidMal, as well as to detect the novel one. We consider few-shot DroidMal detection as DoridMal encrypted network traffic classification and propose an image-based method with meta-learning, namely AMDetector, to address the issues. By capturing network traffic produced by DroidMal, samples are augmented and thus cater to the learning algorithms. Firstly, DroidMal encrypted traffic is converted to session images. Then, session images are embedded into a high dimension metric space, in which traffic samples can be linearly separated by computing the distance with the corresponding prototype. Large-scale and novel DroidMal traffic is classified by applying different meta-learning strategies. Experimental results on public datasets have demonstrated the capability of our method to classify large-scale known DroidMal traffic as well as to detect the novel one. It is encouraging to see that, our model achieves superior performance on known and novel DroidMal traffic classification among the state-of-the-arts. Moreover, AMDetector is able to classify the unseen cross-platform malware. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
With the growing popularity of Android smart devices, and especially with the recent advances brought on by the COVID-19 pandemic on digital adoption and transformation, the importance of protecting these devices has grown, as they carry very sensitive data. Malicious attacks are targeting Android since it is open source and has the highest adoption rate among mobile platforms. Botnet attacks are one of the most often forgotten types of attacks. In addition, there is a lack of review papers that can clarify the state of knowledge and indicate research gaps in detecting android botnets. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of Android Botnet detection. This study attempts to provide a comprehensive overview of the deployed AI apps for future academics interested in performing Android Botnet Detection studies. We focused on the applications of artificial intelligence and its two prominent subdomains, machine learning (ML) and deep learning (DL) techniques. The study presents available Android Botnet datasets suitable for detection using ML and DL algorithms. Moreover, this study provides an overview of the methodologies and tools utilized in APK analysis. The paper also serves as a comprehensive taxonomy of Android Botnet detection methods and highlights a number of challenges encountered while analyzing Android Botnet detection techniques. The research gaps indicated an absence of hybrid analysis research in the area, as well as a lack of an up-to-date dataset and a time-series dataset. The findings of this paper show valuable prospective directions for future research and development opportunities. Author
ABSTRACT
The worldwide population is aging, even though more and more elderly people are living independently and alone. Moreover, pandemics such as COVID-19 are putting enormous pressure in health care systems all around the world. To deal with the growing elderly population and health care challenges, there is an emerging focus on technology and IT products. Technology such as mobile devices with their ever-increasing computational power, and differing sensors show immense usefulness for elderly as well as people with disability. This study proposes the development of 3D QR codes for improved safety, security, and customization of user interfaces. This study proposes multiple QR codes aligned together to create a QR cube (or QR cuboid) to store an increasing amount of information about the user. Individual QR codes will be compatible with existing systems. On the other hand, nearly, all individual information is stored with the user, enabling better privacy. Even with the development of future technologies, QR cubes can be used to access digital information. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In an attempt to curtail and prevent the spread of Covid-19 infection, social distancing has been adopted globally as a precautionary measure. Statistics shows that 75% of appointments most especially in the health sector are being handled by telephone since the outbreak of the Covid-19 pandemic. Currently most patients access health care services in real time from any part of the World through the use of Mobile devices. With an exponential growth of mobile applications and cloud computing the concept of mobile cloud computing is becoming a future platform for different forms of services for smartphones hence the challenges of low battery life, storage space, mobility, scalability, bandwidth, protection and privacy on mobile devices has being improved by combining mobile devices and cloud computing which rely on wireless networks to create a new concept and infrastructure called Mobile Cloud Computing (MCC). The introduction of Mobile cloud computing (MCC) has been identified as a promising approach to enhance healthcare services, with the advent of cloud computing, computing as a utility has become a reality thus a patient only pays for what he uses. This paper, presents a systematic review on the concept of cloud computing in mobile Environment;Mobile Payments and Mobile Healthcare Solutions in various healthcare applications, it describes the principles, challenges and opportunity this concept proffers to the health sector to determine how it can be harnessed is also discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The ongoing Covid-19 pandemic has impacted our everyday lives and demands everyone to take countermeasures such as wearing masks or disinfecting their hands. However, while previous work suggests that these countermeasures may profoundly impact biometric authentication, an investigation of the actual impact is still missing. Hence, in this work, we present our findings from an online survey (n=334) on experienced changes in device usage and failures of authentication. Our results show significant changes in personal and shared device usage, as well as a significant increase in experienced failures when comparing the present situation to before the Covid-19 pandemic. From our qualitative analysis of participants' responses, we derive potential reasons for these changes in device usage and increases in authentication failures. Our findings suggest that making authentication contactless is only one of the aspects relevant to encounter the novel challenges caused by the pandemic. © 2022 ACM.
ABSTRACT
In the context of the COVID-19 pandemic the malicious actors actively creating COVID-themed android malicious apps and without much attention user may often grant all the required permissions to install those fake apps. The Android permissions are crucial sources of vulnerability. This vulnerability often leads to major privacy threats. In this work COVID-themed android malwares were collected and analyzed to develop a detection framework based on the static feature permission and machine learning techniques. The proposed system analyses 100 COVID-themed fake applications which released in 2020. The sensitive permissions are selected using Recursive Feature Elimination (RFE) technique. The study shows better accuracy of 0.830 and 0.812 with Decision tree classifier and Random forest classifier respectively. © 2022 IEEE.
ABSTRACT
In the past two years, the demand for the use of mobile networks has increased, due to what the world has been exposed to in the face of the COVID-19 pan-demic and the lack of communication between people. People resorted to using of mobile networks in light of the pandemic, and their importance has appeared in several aspects, such as automotives, media and entertainment, and healthcare. This growing demand for the mobile network led to the actual development of the 4G network in terms of providing the speed of transmission and encryption data to maintain security. In this paper, a new member of the SNOW 3G family was proposed, which is one of the fourth generation algorithms called SNOW 3G-M, which has a higher encryption speed in line with the capacity of modern CPUs and is expected to be a move to the fifth generation communication system in the future. The SNOW 3G-M keystream passes all of the tests for long key stream data, short key stream data, and initialization vector data sets.
ABSTRACT
The Secure and Efficient Virtual Entry Authorization using AES Encryption Technique can act as a sign in option whenever a public place is visited and this can be really helpful to track anybody's route map, when tested positive for when listed in any primary contact list it is easier to share the route map with the health authorities. Secure and Efficient Virtual Entry Authorization using Advanced Encryption Standard Encryption Technique helps to keep a correct picture of the daily routes and visits, so that it will be able to record and track at any emergency cases. It is possible to add the dear ones as member that means the mobile device is not needed to ensure the route map. In Business, it helps to keep a correct picture of daily visits of the customers in an organization. So that it will be able to record and track at any emergency cases. There is an option to scan the Quick Response code or enter some personal details which helps to record even if the customers don't have a mobile device. © 2021 IEEE.