A review of deep learning models to detect malware in Android applications

Mbunge, Elliot; Muchemwa, Benhildah; Batani, John; Mbuyisa, Nobuhle

doi:10.1016/j.csa.2023.100014

Cited by 12 publications

(5 citation statements)

References 46 publications

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“…The findings of this study are also relevant to research investigating the utilization of interactive simulations in the learning of electrical circuits. Previous studies have demonstrated that the incorporation of interactive simulations can enhance students' conceptual understanding and practical skills [3], [10], [33]. These findings are in line with the results of our research, wherein Android-based courseware offers a simulation feature that aids students in comprehending practical aspects of electrical circuits.…”

Section: Discussionsupporting

confidence: 91%

Evaluating the Practicality of Android-Based Courseware in Enhancing Electrical Circuit Proficiency among Vocational Students

Yanto,

Ganefri,

Sukardi

et al. 2024

Int. J. Interact. Mob. Technol.

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The evaluation and analysis of the practicality of Android-based courseware in enhancing vocational students’ understanding of electrical circuits (EC) is the primary focus of this research. A quantitative survey-based research approach was employed, utilizing the Practicality Assessment Instrument to evaluate the practicality level of the Android-based courseware among students. The collected data will undergo statistical analysis using descriptive analysis techniques. The practicality assessment results for each aspect will be calculated as a percentage and grouped into various categories. The findings reveal a high practicality level across different aspects, namely 90.19% for Availability and Accessibility, 89.88% for Performance and Responsiveness, 86.96% for Content Compatibility and Completeness, and 90.06% for Functionality and Resource Utilization. These outcomes demonstrate that the Android-based courseware serves as a highly practical learning medium for enhancing the understanding of electrical circuits. The integration of Android-based technology in the educational environment has proven to be effective and beneficial. These findings offer valuable insights for educators, instructional designers, and stakeholders to enhance modern learning environments. Future research can further investigate the impact of Android-based courseware on learning outcomes and explore additional practical dimensions to comprehensively evaluate its effectiveness.

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Section: Discussionsupporting

confidence: 91%

Evaluating the Practicality of Android-Based Courseware in Enhancing Electrical Circuit Proficiency among Vocational Students

Yanto,

Ganefri,

Sukardi

et al. 2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…An essential and intricate aspect of organizing malware analysis lies in the comprehensive consideration of all aspects and attributes of Android applications, while safeguarding the retention of critical features. Several recent review papers provide comprehensive overviews of past works and research efforts in Android malware analysis [8][9][10][11]. This assumes paramount significance and complexity, notably due to increase in use of obfuscation techniques.…”

Section: Related Workmentioning

confidence: 99%

Android Malware Detection using HexCode Features

Anand,

Singh,

Dhoundiyal

2024

Preprint

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With the widespread adoption of smartphones, Android has emerged as a preferred and highly targeted platform by malware. The proliferation of malware for Android devices has been exponential and to counter this Android malware detection together with familial classification has to be automated. This paper introduces a dual-pronged approach for Android malware detection and familial classification. The proposed approach employs a static analysis approach to extract Java ARchive (JAR) files from Android application packages (APKs). Our methodology involves utilizing extensive hex strings derived from JAR files and applying n-gram sliding window technique to extract features. To validate the robustness of our model and assess its versatility, we employed both standard and obfuscated malware datasets. A range of machine learning models, including Naive Bayes(NB), Random Forest(RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) and a Convolutional Neural Network (CNN) for familial classification, were employed. The experiments encompassed non-obfuscated malware samples (5560), obfuscated malware samples (15479), and benign samples (6200). Additionally, we conducted a comparative analysis of our model's performance against existing methods, including those based on deep learning.

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“…The deep learning techniques often provide superior performance over machine learning-based solutions when there is a large dataset. Mbunge et al [29] suggest that there is a need to increase malware dataset sizes as well as improve the accessibility of malware datasets to the public.…”

Section: Literature Reviewmentioning

confidence: 99%

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

Rahima Manzil,

Naik S

2024

Cloud Computing and Data Science

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The increasing prevalence of Android malware poses significant risks to mobile devices and user privacy. The traditional detection methods have limitations in keeping up with the evolving landscape of malware attacks, necessitating the development of more effective solutions. In this paper, we present DeepMetaDroid, a real-time detection approach for Android malware that leverages metadata features. By analyzing crucial metadata, including APK size, download size, permissions, certificates, and DEX files, the proposed method enables effective identification of malware and enhances mobile security. Using deep learning techniques, a lightweight Android real-time monitoring system is equipped with the trained model. These methods include long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and other ensemble models. Utilizing the rectified linear unit (ReLU) as the activation function, the DNN model is constructed with 32 neurons in the input layer. A one-dimensional convolutional layer with 32 neurons and a filter size of three is used as the input layer in the CNN model. The LSTM model is designed with an input layer consisting of 16 neurons. The GRU model with 32 neurons is employed in the input layer. Additionally, ensemble models that combined several architectures were developed. The proposed method offers a faster and more scalable solution for malware detection by consuming fewer resources like memory and CPU. This work ensures device security by providing real-time monitoring on Android devices to prevent users from installing malicious applications and, thus, enhance user privacy and security.

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A review of deep learning models to detect malware in Android applications

Cited by 12 publications

References 46 publications

Evaluating the Practicality of Android-Based Courseware in Enhancing Electrical Circuit Proficiency among Vocational Students

Evaluating the Practicality of Android-Based Courseware in Enhancing Electrical Circuit Proficiency among Vocational Students

Android Malware Detection using HexCode Features

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

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