In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android. Ransomware extorts victims for a sum of money by taking control of their ...devices or files. In light of their rapid growth, there is a pressing need to develop effective countermeasure solutions. However, the research community is still constrained by the lack of a comprehensive data set, and there exists no insightful understanding of mobile ransomware in the wild. In this paper, we focus on the Android platform and aim to characterize existing Android ransomware. Specifically, we have managed to collect 2,721 ransomware samples that cover the majority of existing Android ransomware families. Based on these samples, we systematically characterize them from several aspects, including timeline and malicious features. In addition, the detection results of existing anti-virus tools are rather disappointing, which clearly calls for customized anti-mobile-ransomware solutions. To detect ransomware that extorts users by encrypting data, we propose a novel real-time detection system, called RansomProber. By analyzing the user interface widgets of related activities and the coordinates of users' finger movements, RansomProber can infer whether the file encryption operations are initiated by users. The experimental results show that RansomProber can effectively detect encrypting ransomware with high accuracy and acceptable runtime performance.
Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around 1 million new malware ...samples per quarter, and it was reported that over 98% of these new malware samples are in fact "derivatives" (or variants) from existing malware families. In this paper, we first show that runtime behaviors of malware's core functionalities are in fact similar within a malware family. Hence, we propose a framework to combine "runtime behavior" with "static structures" to detect malware variants. We present the design and implementation of Monet, which has a client and a backend server module. The client module is a lightweight, in-device app for behavior monitoring and signature generation, and we realize this using two novel interception techniques. The backend server is responsible for large scale malware detection. We collect 3723 malware samples and top 500 benign apps to carry out extensive experiments of detecting malware variants and defending against malware transformation. Our experiments show that Monet can achieve around 99% accuracy in detecting malware variants. Furthermore, it can defend against ten different obfuscation and transformation techniques, while only incurs around 7% performance overhead and about 3% battery overhead. More importantly, Monet will automatically alert users with intrusion details so to prevent further malicious behaviors.
The purpose of this study was to develop English learning media based on an Android application for X SMA compliments material, as well as to determine the quality of media products. This study uses ...Research and Development (R&D) research and the model used is the ADDIE Development model which consists of 5 stages namely Analysis, Design, Development, Implementation and Evaluation. The data analysis technique used is feasibility and quality the media obtained with the instrument in the form of a questionnaire. This product has been validated by material experts, media experts. The results of the validation show that material experts obtain an average of 4.77 which is included in the "Very Good" criteria, for the validation results of media experts obtain an average of 4.56 in the "Very Good" category. The developed media was tested on 26 class X SMA Muhammadiyah 1 Pekanbaru. Based on the acquisition results these data indicate that Android-Based English Learning Media in Material Compliments is suitable for use as a learning resource for high school students. In trial with students obtaining a result of 4.61 which is included in the "Very Good" criteria. It can be concluded that this android-based application product can increase student motivation and achievement and is suitable for use in the learning process.
Purpose: This research was created to develop accounting learning media to increase student motivation and learning outcomes and as an alternative learning media for teachers. The media was developed ...using the Android-based Canva application at SMK N. 8 Palembang. Media development procedures go through validation tests, practical tests, and effectiveness tests on teaching and learning activities and student learning outcomes.
Design/methodology/approach: The research method uses development research using 2 development theories, at the development stage using the Rowntree Model and the evaluation stage using Tessmer. Collecting research data using interviews, questionnaires, observations, and tests in the process of design, development, and evaluation. At the stage after developing the learning media prototype the researcher conducted tests involving learning material experts, learning media design experts, and learning media experts to find out the validation of the media that was made.
Findings: The results of the validation test of 4.41 were stated to be very valid. Furthermore, a field validation test was carried out on students at SMK N.8 Palembang for each student (one-to-one) and in small groups based on student observations it was found that 100% practicality was very practical. In the final stage of implementing it in a full class by observing student responses using observation, at the end of the lesson a series of tests were carried out to determine the effect of the media on learning outcomes at 85.71% very good criteria.
Practical implications: Based on the research, it can be concluded that Android-based accounting learning media has fulfilled the requirements for validity, practicality and has a good potential effect on student learning activities and outcomes. So that Android-based accounting learning media can be used as an alternative teacher in teaching accounting material for adjusting journal entries.
Paper type: Research paper
•The accuracy of the proposed model was calculated as high as 0.9.•A novel 1-dimensional CNN model was proposed.•The features were automatically selected thanks to the proposed model.•The experiments ...were conducted on the de facto datasets.•We shed light on the insights of Android malware through the conducted experiments.
Smartphones have become an integral part of our daily lives thanks to numerous reasons. While benefitting from what they offer, it is critical to be aware of the existence of malware in the Android ecosystem and be away from them. To this end, an end-to-end and highly effective Android malware detection framework based on CNN, namely, DroidMalwareDetector, was proposed within this study. Unlike most of the related work, DroidMalwareDetector was specifically designed to (i) automate feature extraction and selection, (ii) propose a novel CNN that operates with 1-dimensional data, and (iii) use intents and API calls alongside the widely used permissions to perform comprehensive malware analysis. The proposed framework was trained and evaluated on the constructed dataset, which consisted of 14,386 apps from the de-facto standard datasets. The proposed framework’s efficiency in terms of distinguishing malware from benign apps was revealed thanks to the conducted experiments. According to the experimental result, the accuracy of the proposed framework was calculated as high as 0.9, which was higher than the accuracy values obtained from a wide range of machine learning algorithms. The insights which were gained through the conducted experiments were revealed as another contribution to the research field.
Android malware has continued to grow in volume and complexity posing significant threats to the security of mobile devices and the services they enable. This has prompted increasing interest in ...employing machine learning to improve Android malware detection. In this paper, we present a novel classifier fusion approach based on a multilevel architecture that enables effective combination of machine learning algorithms for improved accuracy. The framework (called DroidFusion), generates a model by training base classifiers at a lower level and then applies a set of ranking-based algorithms on their predictive accuracies at the higher level in order to derive a final classifier. The induced multilevel DroidFusion model can then be utilized as an improved accuracy predictor for Android malware detection. We present experimental results on four separate datasets to demonstrate the effectiveness of our proposed approach. Furthermore, we demonstrate that the DroidFusion method can also effectively enable the fusion of ensemble learning algorithms for improved accuracy. Finally, we show that the prediction accuracy of DroidFusion, despite only utilizing a computational approach in the higher level, can outperform stacked generalization, a well-known classifier fusion method that employs a meta-classifier approach in its higher level.
Menurut WHO sebanyak 7,3% gizi buruk karena kurangnya pengetahuan ibu tentang tumbuh kembang balita. Penelitian ini bertujuan untuk menganalisis peningkatan pengetahuan ibu tentang tumbuh kembang ...balita melalui aplikasi berbasis android. Jenis penelitian quasy eksperimen dengan rancangan pre-test dan post-test with control group design. Populasi dalam penelitian ini sebanyak 200 dengan perhitungan sampel Vincent gasper sebanyak 54 ibu pada setiap kelompok. Instrumen penelitian menggunakan kuesioner, dianalisis menggunakan uji wilcoxon. Hasil selisih skor pengetahuan sebelum dan sesudah intervensi sebesar 22,28 dengan p value=0,000. Kesimpulan dari penelitian ini adalah, aplikasi ini sebagai media edukasi tentang tumbuh kembang balita mampu meningkatkan pengetahuan ibu.Â
Android based smartphones have become a top target for malware writers due to their widespread use. A number of malicious applications are present on play stores and downloaded on daily basis, posing ...a significant threat to users’ personal and business data. As a result, the design of malware analysis frameworks is crucial in protecting the growing number of users who rely on their smart phones for routine and business tasks. The traditional signature based schemes for malware detection are unable to handle new and sophisticated malware. Furthermore, generic solutions based on static analysis schemes become less effective in the presence of obfuscated malware. In this study, a dynamic analysis based framework, VolMemDroid, for detecting malicious applications for Android is proposed. The framework extracts the volatile memory artifacts for profiling malicious Android applications. For this purpose, the memory forensic framework of volatility is utilized. A number of volatility plugins are analyzed for their compatibility w.r.t the Android platform and their ability in modeling the application’s behavior. After testing a number of plugins, chosen plugins are further processed for extraction of features. A comprehensive feature set for Android malware detection and categorization is proposed. It has been found that the suggested framework is effective for detecting Android malicious applications with an F1-score of 0.972, which is better than existing volatile memory based approaches for Android malware detection. The framework is also found to be effective in categorizing malicious Android applications into four distinct classes.
•A novel data driven malware detection and categorization framework for Android.•Resilient against obfuscation by utilizing volatile memory-based features.•First study to profile Android apps using memory forensics framework of volatility.•Volatility Plugins are analyzed for generation of useful feature representations.•Comprehensive feature set is reported for effective Android application analysis.
In parallel with the meteoric rise of mobile software, we are witnessing an alarming escalation in the number and sophistication of the security threats targeted at mobile platforms, particularly ...Android, as the dominant platform. While existing research has made significant progress towards detection and mitigation of Android security, gaps and challenges remain. This paper contributes a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area. We have carefully followed the systematic literature review process, and analyzed the results of more than 300 research papers, resulting in the most comprehensive and elaborate investigation of the literature in this area of research. The systematic analysis of the research literature has revealed patterns, trends, and gaps in the existing literature, and underlined key challenges and opportunities that will shape the focus of future research efforts.
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