Transparent Peer Review By Scholar9
Advanced Malware Detection: Leveraging Hybrid Machine Learning and Deep Learning Models on App Metadata
Abstract
As mobile applications become more widespread, the risk of malware threats has also escalated, creating an urgent need for more advanced detection techniques to protect both user data and system stability. This paper presents a sophisticated malware detection system that combines machine learning and deep learning methods to improve both the accuracy and efficiency of threat detection. The system utilizes a wide array of application characteristics—such as size, download frequency, pricing, categories, update history, version details, user reviews, and content types—to detect and classify potential malware. By employing a variety of algorithms, including Random Forest, Support Vector Machines (SVM), Decision Trees, and Logis- tic Regression, in conjunction with deep learning models, the system achieves superior performance over traditional detection techniques. Extensive experiments were conducted to assess the effectiveness of these methods, with the results illustrated through bar graphs, pie charts, and histograms. This research not only provides a comparative evaluation of multiple detection tech- niques but also contributes to enhancing cybersecurity strategies within the ever-evolving realm of mobile applications.
Amit Mangal Reviewer
23 Sep 2024 02:28 PM
Approved
Relevance and Originality
The study addresses a critical and timely issue in cybersecurity, focusing on the rising threat of malware in mobile applications. By proposing an advanced detection system that integrates machine learning and deep learning methods, the research presents original contributions to improving malware detection. The emphasis on a wide range of application characteristics enhances its relevance to contemporary cybersecurity challenges.
Methodology
The combination of machine learning and deep learning techniques, alongside the use of various algorithms such as Random Forest, SVM, Decision Trees, and Logistic Regression, demonstrates a robust methodological framework. The focus on diverse application characteristics for malware detection is commendable. However, more details on the experimental design, including sample size and data sources, would enhance the transparency and rigor of the methodology.
Validity & Reliability
The extensive experiments conducted to evaluate the detection system's effectiveness are a strong point. However, providing information on the dataset used—such as its diversity and size—would strengthen the validity of the findings. Discussing performance metrics and validation methods employed during analysis would also enhance reliability.
Clarity and Structure
The summary effectively conveys the study's objectives, methodologies, and outcomes. The use of visual aids, such as bar graphs and pie charts, to illustrate results adds clarity. However, a more structured format with clearly defined sections—such as introduction, methodology, results, and conclusion—would improve overall readability and organization.
Result Analysis
The findings highlight the superior performance of the proposed detection system compared to traditional methods, which is significant for enhancing cybersecurity. Providing specific quantitative results, such as accuracy rates or detection speeds, would offer more context for the effectiveness of the system. Additionally, discussing the implications of these results for future research and practical applications in the industry would enrich the analysis and underscore the study's relevance.
IJ Publication Publisher
Done Sir
Amit Mangal Reviewer