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.
Uma Babu Chinta Reviewer
23 Sep 2024 02:19 PM
Approved
Relevance and Originality
The paper addresses a critical and timely issue in cybersecurity, focusing on the rising threat of malware in mobile applications. The integration of machine learning and deep learning techniques for malware detection is both relevant and innovative, contributing valuable insights to the field. By exploring a variety of application characteristics, the study offers a unique perspective on enhancing threat detection.
Methodology
The methodology is comprehensive, employing various algorithms such as Random Forest, SVM, Decision Trees, and Logistic Regression, alongside deep learning models. This diverse approach enhances the robustness of the detection system. However, more details on the experimental design, including how the data was collected and any specific evaluation metrics used, would strengthen the methodology section.
Validity & Reliability
The extensive experiments conducted to assess the system's effectiveness suggest a solid foundation for validity. Yet, more information regarding the dataset—such as its size, diversity, and sources—would enhance the credibility of the findings. Additionally, discussing the measures taken to ensure reliability, such as cross-validation techniques, would provide further assurance regarding the results.
Clarity and Structure
The structure of the paper is logical, progressing from the problem statement to the proposed solution. However, clarity could be improved by refining some complex sentences and ensuring consistent terminology. Clearer section headings and transitions would also enhance readability, helping guide the reader through the discussion.
Result Analysis
The results are presented using visual aids like bar graphs, pie charts, and histograms, which effectively illustrate findings. However, accompanying textual explanations of these visuals would enhance understanding. A more detailed analysis of the results, including statistical significance and performance comparisons among algorithms, would provide deeper insights into the proposed system's effectiveness and its implications for cybersecurity strategies.
IJ Publication Publisher
Thank You Sir
Uma Babu Chinta Reviewer