Android Application Software Research Articles

Benchmarking Android Malware Analysis Tools

Today, malware is arguably one of the biggest challenges organisations face from a cybersecurity standpoint, regardless of the types of devices used in the organisation. One of the most malware-attacked mobile operating systems today is Android. In response to this threat, this paper presents research on the functionalities and performance of different malicious Android application package analysis tools, including one that uses machine learning techniques. In addition, it investigates how these tools streamline the detection, classification, and analysis of malicious Android Application Packages (APKs) for Android operating system devices. As a result of the research included in this article, it can be highlighted that the AndroPytool, a tool that uses machine learning (ML) techniques, obtained the best results with an accuracy of 0.986, so it can be affirmed that the tools that use artificial intelligence techniques used in this study are more efficient in terms of detection capacity. On the other hand, of the online tools analysed, Virustotal and Pithus obtained the best results. Based on the above, new approaches can be suggested in the specification, design, and development of new tools that help to analyse, from a cybersecurity point of view, the code of applications developed for this environment.

Employing combined spatial and frequency domain image features for machine learning-based malware detection

<p>The ubiquitous adoption of Android devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. The core idea of this framework was the conversion of text-based data extracted from Android APKs into grayscale images. The novelty aspect of this work lied in the unique image feature extraction strategies and their subsequent hybridization to achieve accurate malware classification using ML models. More specifically, four distinct feature extraction methodologies, namely, Texture and histogram of oriented gradients (HOG) from spatial domain, and discrete wavelet transform (DWT) and Gabor from the frequency domain were employed to hybridize the features for improved malware identification. To this end, three image-based datasets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, including naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were employed for the classification task. The experimental results demonstrated the efficacy of the proposed framework, achieving a peak classification accuracy of 93.03% and recall of 97.1% for the RF classifier using the Manifest dataset and a combination of Texture and HOG features. These findings validate the proof-of-concept and provide valuable insights for researchers exploring ML/deep learning (DL) approaches in the domain of AMD.</p>

Noise Detection System in The Classroom Using Sound Sensors and NodeMCU ESP6288

The educational environment is the process of educational activities, so the environment is expected to be comfortable and avoid noise. Noise can be disturbing, such as the ringing of mobile phones or sounds produced by humans. Sound noise in a room can cause loss of concentration, so that indoor activities can be disrupted. Based on these problems, it is necessary to have a tool used to detect the noise level of sound in the classroom. Therefore, a noise detection system in the learning room uses a sound sensor based on NodeMCU ESP8266, which can send email notifications and be monitored through the thinger.io application. The location of this research is in a classroom. This tool consists of hardware (hardware) including NodeMCU ESP8266, PIR Sensor, LCD 16x2 I2C, Sound Sensor, DF Player Mini Mp3, Speaker, and Android application software. The working principle is that if the decibel value exceeds the predetermined value, the room user will hear a warning sound and send an informational text in the form of an email notification. Based on tests on several parameters, testing using the sound of a drilling machine obtained an error value of 8.7%. In music sounds, an error value of 7.7%, and inhuman screaming sounds, an error value of 8.1%. The tool has been successful in the test results and can be implemented in the classroom.

Adaptive Android APKs Reverse Engineering for Features Processing in Machine Learning Malware Detection

The key component that makes the detection of android malware possible is the availability of the right triggers and pointers, which are found in the Android application packages, known as features or attributes. These are fundamental in the training of the different machine learning algorithms to produce the required detection model. The process of extracting these attributes or features, from the Android application packages, is known as reverse engineering. This paper delved into the experimental detail processes of applying reverse engineering procedure, using Sublime Text 2 and Androguard Plugin, on Android Application packages for the extraction of, particularly permissions, which are the targeted features. The study further discussed the cleaning stages, using NotePad++, Microsoft Excel Worksheet, and MS Word, to sort out all the relevant and important features by removing all the noisy ones. A total of 1500 Android apps were downloaded from both benign and malicious sources and used for the experiment. The cleaned or important features extracted from these application packages at the end of the reverse engineering processes are 162 in total and these were further used to form a feature binary matrix of size 1500 by 163 (including the class features).

Development of GMVIT College App Using Android Studio

Abstract: The area of mobile technology opens the windows to the android app. It is time to change from the standard websites to the app, which has become part of our daily routine. We are introducing GMVIT.apk the android application software which would be the miniature of our college website. The server allows the faculty to pass the data and the students can download the required documents through the android device. This project is performed for the sake of reducing efforts between both the students and the faculty.

Medication Alert - an Android Application

This is an android application software that alerts users with an automatic alarming system for medication and dosages. Most of the time patients cannot remember the exact time when they need to take the medicine. This app will alert patients about the time of medication, description of medicine, before-after meals, and so on. The alarm can be set with different types of pictures with the name of the medicines to overcome any kind of difficulty in reading. To develop this project, an incremental development strategy is followed to support customer requirements. Android Studio IDE, Java, and XML tools will be used for the implementation of the project. The system focuses on easy navigation and a good user interface. Many systems have been developed where new hardware and data storage is required but, in our work, we have made an attempt to develop a system that is economical, time-saving, and supports medication adherence.

DESIGN OF WHEEZING SOUND DETECTION WEARABLE DEVICE BASED ON INTERNET OF THINGS

<p><strong>Introduction: </strong>Wheezing is one of the most common manifestations of airway obstruction. The use of a stethoscope in the wheezing examination has several disadvantages such as subjective results and depends on the auditor's hearing sensitivity. So an easy device is needed that helps determine the wheezing sound precisely. This study assembled a single tool to detect wheezing sounds based on the internet of things.<strong></strong></p><p><strong>Method</strong><strong>: </strong>This tool is designed with a microprocessor hardware connected to an electric stethoscope so that it can be attached to the patient's chest wall. Collection of chest breathing voice data accessed on kaggle.com. The creation of algorithms with Convolutional Neural Networks (CNN) was later changed to Mel Frequency Cepstral Coefficients (MFCC). This model will be implanted in a microprocessor and use python language to be able to record the sound of chest wall vibrations. The recorded sound is converted into MFCC to make it easier to perform wheezing sound detection. MFCC image results and detection results are sent to the database via the firebase database feature which stores MFCC photos in real-time as they are detected. Designing android application software using Flutter builds communication between android applications and firebase databases that allows applications to retrieve MFCC images as the final result. <strong></strong></p><p><strong>Result</strong><strong>: </strong>The results of the tool trial on five volunteers, three exacerbation asthma patients and two healthy people showed the device can detect wheezing sounds at a frequency of 400Hz with 80% accuracy through CNN and MFCC algorithms Internet of things based.<strong></strong></p><p><strong>Conclusion</strong><strong>: </strong>This tool can help health workers to accurately determine wheezing sounds, enforce the diagnosis faster, the prognosis of the disease to be better, so as to reduce the number morbidity and mortality of diseases with airway abnormalities in Indonesia<strong></strong></p><div> </div>

Ensemble Framework Combining Family Information for Android Malware Detection

Abstract Each malware application belongs to a specific malware family, and each family has unique characteristics. However, existing Android malware detection schemes do not pay attention to the use of malware family information. If the family information is exploited well, it could improve the accuracy of malware detection. In this paper, we propose a general Ensemble framework combining Family Information for Android Malware Detector, called EFIMDetector. First, eight categories of features are extracted from Android application packages. Then, we define the malware family with a large sample size as a prosperous family and construct a classifier for each prosperous family as a conspicuousness evaluator for the family characteristics. These conspicuousness evaluators are combined with a general classifier (which can be a base or ensemble classifier in itself), called the final classifier, to form a two-layer ensemble framework. For the samples of prosperous families with conspicuous family characteristics, the conspicuousness evaluators directly provide detection results. For other samples (including the samples of prosperous families with nonconspicuous family characteristics and the samples of nonprosperous families), the final classifier is responsible for detection. Seven common base classifiers and three common ensemble classifiers are used to detect malware in the experiment. The results show that the proposed ensemble framework can effectively improve the detection accuracy of these classifiers.

FG-Droid: Grouping based feature size reduction for Android malware detection.

BackgroundThe number of applications prepared for use on mobile devices has increased rapidly with the widespread use of the Android OS. This has resulted in the undesired installation of Android application packages (APKs) that violate user privacy or are malicious. The increasing similarity between Android malware and benign applications makes it difficult to distinguish them from each other and causes a situation of concern for users.MethodsIn this study, FG-Droid, a machine-learning based classifier, using the method of grouping the features obtained by static analysis, was proposed. It was created because of experiments with machine learning (ML), deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)-based models using Drebin, Genome, and Arslan datasets.ResultsThe experimental results revealed that FG-Droid achieved a 97.7% area under the receiver operating characteristic (ROC) curve (AUC) score with a vector including only 11 static features and the ExtraTree algorithm. While reaching a high classification rate, only 0.063 seconds were needed for analysis per application. This means that the proposed feature selection method is faster than all traditional feature selection methods, and FG-Droid is one of the tools to date with the shortest analysis time per application. As a result, an efficient classifier with few features, low analysis time, and high classification success was developed using a unique feature grouping method.

The Malware Detection Approach in the Design of Mobile Applications

Background: security has become a major concern for smartphone users in line with the increasing use of mobile applications, which can be downloaded from unofficial sources. These applications make users vulnerable to penetration and viruses. Malicious software (malware) is unwanted software that is frequently used by cybercriminals to launch cyber-attacks. Therefore, the motive of the research was to detect malware early before infection by discovering it at the application-design level and not at the code level, where the virus will have already damaged the system. Methods: in this article, we proposed a malware detection method at the design level based on reverse engineering, the unified modeling language (UML) environment, and the web ontology language (OWL). The proposed method detected “Data_Send_Trojan” malware by designing a UML model that simulated the structure of the malware. Then, by generating the ontology of the model, and using RDF query language (SPARQL) to create certain queries, the malware was correctly detected. In addition, we proposed a new classification of malware that was suitable for design detection. Results: the proposed method detected Trojan malware that appeared 552 times in a sample of 600 infected android application packages (APK). The experimental results showed a good performance in detecting malware at the design level with precision and recall of 92% and 91%, respectively. As the dataset increased, the accuracy of detection increased significantly, which made this methodology promising.

PENGEMBANGAN APLIKASI ANDROID BERBANTU SOFTWARE ISPRING SUITE SEBAGAI MEDIA PEMBELAJARAN EKONOMI MADRASAH ALIYAH MA’ARIF 9 KOTAGAJAH TAHUN PELAJARAN 2018/2019


 Learning media is one of the needs that must be fulfilled in the learning process. The results of the pre-survey of learning media used in the Aliyah Ma'arif 9 Kotagajah Madrasah, researchers found several problems, namely the use of learning media that were less innovative in learning and the limitation of images in teaching books, worksheets and powerpoints so that students were not attracting attention in the learning process . So that learning media are needed that can attract the attention of students such as android applications, android application software assisted ispring suite as an economic learning media arranged concisely, clearly, and made in a practical form so that students are more interested and enthusiastic in learning and can understand the material. The development model used in this development is the ADDIE development model which consists of several stages, namely (Analysis, Design, Development, Implementation, and Evaluation). Based on the results of the development of android applications assisted by software ispring suite as an economic learning media it is known the results of recapitulation of data that have been carried out by learning media material experts on android applications with an average score of 86% included in the very feasible category and declared valid, the results of recapitulation from media experts with scores on average 87% fall into the category of very decent and declared valid. Furthermore, the practicality test by IPS X grade 1 students of Aliyah Ma'arif 9 Kotagajah gets an average score of 92.2% and falls into the very practical category so that it is declared practical. research shows that economic learning media using this android application is suitable for use in learning. Thus it can be concluded that the learning media of android applications are learning media that are valid and practical to be used in the learning process.

Validity testing of blended learning based on android smartphones in computer device maintenance course

This study aimed to determine the validity level of blended learning media to support the blended learning process. This was a Research and Development (R&D) using the 4D development model. The model involved four steps, namely: 1) Define, 2) Design, 3) Development, and 4) Assessment. Based on the research that has been done, the average value of the test assessment for Android media to support the learning process through Blended Learning is 94.28% which level of validity can be interpreted as very valid. Based on the assessment and input from material experts, Android application software experts and multimedia experts, it can be concluded that the blended learning media is suitable for learning the maintenance of computer devices in the Informatics Engineering Education Department.

Information System Design for Calculating the Reliability of Electricity Distribution System in Pekalongan Substation Based on Android OS

Reliability of distribution networks is a factor that greatly affects customers as consumers of electricity. Analyzing and calculating the reliability of distribution networks are determined by the reliability index including SAIFI, SAIDI, CAIDI, and ASAI. On the other hand, smartphone technology is growing rapidly with a variety of applications to help simplify and accelerate human work in several fields of work. This paper delivers the design of an Android-phone-based analytic tool for distribution system reliability index measurement by developing it on Android application software. This application is named KALINDA, stands for Kalkulator Indeks Keandalan (Reliability Index Calculator), and created by using Android Studio IDE. We compare the data result between KALINDA calculations and manual calculations. The results obtained from the KALINDA application are declared to be valid accurate.

The design and feasibility test of drive-thru market prototype based on Wemos as the learning media of internet of things (IoT) application

This research aims at (1) developing a learning media for the internet of things (IoT) applications in the form of Wemos-based drive-thru market module which is appropriate for Interface Engineering Courses at Electrical Engineering Education, Faculty of Engineering, Universitas Negeri Yogyakarta; and (2) determining the feasibility of the developed learning media. The subject of the research was the 2015 Mechatronics Engineering Education students. The object of the research was the drive-thru market prototype. The research instrument was the questionnaire to obtain data on media feasibility. The proper research results were in the form of (1) the hardware namely the drive-thru market prototype and it's Android application software, (2) the proper function of the prototype and Android applications with the design, (3) the feasibility from the media experts with the percentage of 78% and 83 % respectively that can be categorized as Appropriate, and from the user obtained the percentage of 86.15% with the category of Very Appropriate.

Voice Controlled Notice Board Display with Android Application

Now-a-days notice board plays a crucial role in many institutes/organizations or in railway stations, bus stands, schools, hospitals and in many other public places. Paper notices stacked on the notice board is a time lagging, cost effective process which includes wastage of time, paper and human work. The main purpose of the notice board is used to view the information in an efficient way to the people, on the other hand to update the messages immediately is not easy on the notice board. This project, deals about an advanced Hi-Tech wireless communicated Notice Board. We are introducing a new “VoiceTotext.apk” android application software which would convert the voice to text. It works on all android platforms, but also it can work with a working internet in the mobiles or any other PC’s.

PARUDroid: Validation of Android Malware Detection Dataset

Android has gained its popularity due to its open nature and number of free apps in its play store. Till date, Android has captured 87% of the total market share. 2.8 million apps are present in the official market of Android. Android apps depend upon permissions for its proper functioning. This dataset contains distinct 5,60,142 Android apps that belong to thirty different categories. These Android application packages (.apk) is collected from Google-play store and other promised repositories. In this study, we performed a dynamic analysis of these collected .apk packages and extracted features, i.e., PARU (Permissions, API calls, Rating of an app, and Users download the app). As per the knowledge, this is the first dataset that extracted features by using the Android 6.0 (API 23) version as an Android operating system. The paper discusses the potential usefulness of the dataset for future research in the field of cybersecurity. Further, to check the potential of our dataset, in this research paper malware detection model is developed by using five different classification machine-learning algorithms. Experiment result reveals that model developed using Deep Neural Network (DNN) can able to detect 98.8% malware-infected apps. Dataset URL: http://dx.doi.org/10.17632/mg5c8jxbhm.2

CM-Droid: Secure Container for Android Password Misuse Vulnerability

Android applications are associated with a large amount of sensitive data, therefore application developers use encryption algorithms to provide user data encryption, authentication and data integrity protection. However, application developers do not have the knowledge of cryptography, thus the cryptographic algorithm may not be used correctly. As a result, security vulnerabilities are generated. Based on the previous studies, this paper summarizes the characteristics of password misuse vulnerability of Android application software, establishes an evaluation model to rate the security level of the risk of password misuse vulnerability and develops a repair strategy for password misuse vulnerability. And on this basis, this paper designs and implements a secure container for Android application software password misuse vulnerability: CM-Droid.

Can One Drink Informal Water Alternatives Directly? Using Mobile Phone to Establish Trust in Quality

The informal water market in cities within the Global South is expanding, thanks to drought associated water shortage challenges and other socio-hydrological factors. A midst the growth is the inherent information signal asymmetry driven mainly by the vendors’ unwillingness to share the actual quality data of their source water with their customers. As a result a big mistrust environment has been created as currently; the customers have no mechanism to verify the water quality in real time. This paper aimed at developing an android application software system to fill the gap. The system is to operate the water vending business landscape as a trusted social network site (SNS) using handheld mobile phone devices. An Agile-Scrum methodology was utilized as it allows for quick changes to the system as necessary. An android platform was chosen as the initial Operating System (OS) to run the software system due to its faster global outreach capability. Specifically, Android Studio 3.4.2 IDE running on Windows 10 was deployed. And the primary languages used within the IDE were; Kotlin for the functionality and XML for the user interface (UI). Additionally, the Firebase SDK tools were used for cloud-based database functionality. The results of the prototype include; user side access and feedback exchanges, backend side supports and other added functionalities. The paper is of the strong view that since the system works on anytime-anywhere modality, then it is possible that one can drink the informal water directly. The system is recommended for full scale trial in the affected cities.

Discovering and understanding the vector field using simulation in android app

An understanding of vector field’s concepts are fundamental parts of the electrodynamics course. In this paper, we use a simple simulation that can be used to show qualitative imaging results as a variation of the vector field. Android application packages the simulation with consideration of the efficiency of use during the lecture. In addition, this simulation also trying to cover the divergences and curl concepts from the same conditions that students have a complete understanding and can distinguish concepts that have been described only mathematically. This simulation is designed to show the relationship between the field magnitude and its potential. This application can show vector field simulations in various conditions that help to improve students’ understanding of vector field concepts and their relation to particle existence around the field vector.

Research on Android Application Package Stealth Download Hijacking

Nowadays during the distributing and downloading of Android application packages, it is always be vulnerable to download hijacking attacks. Traffic analysis could be used by sites to detect if they are under this kind of regular download hijacking attacks. Unlike the regular ones, the stealth download hijacking attacks cannot be discovered by using such a method. By studying in an actual case, this paper presents a vulnerability of android application package download hijacking, which can be exploited to implement a stealth download hijacking by deploying bypass devices. And the victim sites can hardly notice it by using current methods. The cause, influence and mechanism of the exploit are discussed in this paper, and we also strive to give a solution for it.