Highlights

  • ISSN Number : 2584-0673

  • Four Issues Per Year

  • No Publication Fee 

  • Free and Open Access Peer Reviewed Journal  

  • Articles Invited in English only 

  • Submission to a Final Decision: 45 Days

  • Acceptance to Publication: 12 Days

Track manuscript
Enter only the numeric value. (eg. Enter 2014000001, not IJIMSR2014000001) and click on Track
Abstract
A Scalable Malware Detection Approach through Significant Permission Identification for Android Devices - Volume -2 | Issue - 1 | 2024, (JAN-MAR)

Pages:24-29

Category: Engineering & Technology

Published Date: 15-May-2024

Graphical Abstract

Praveen Kumar Karri, D.Jaya Kumari, Sowmya Sree Karri

Keywords:

Smartphones, Malicious Software, Android, Machine Learning, Scalable Detection, Mobile security

Abstract:
The global ubiquity of smartphones has led to the availability of many free apps for gaming, communication, financial, and educational needs. However, hazardous malicious software targeting smartphones has increased as the global adoption of these devices has grown. Malware is growing rapidly, with reports predicting a new Android app every 10 seconds, threatening the mobile ecosystem. Due to Android's versatility, users can install apps from third-party app shops and file-sharing websites, compounding malware outbreaks. The seriousness of this situation requires scalable malware detection. Based on permission usage analysis, this project introduces Significant Permission Identification (SigPID), a novel malware detection technique. SigPID uses a three-tiered permission pruning mechanism to discover the most important permissions for distinguishing benign from malicious apps, unlike standard methods that scan all Android permissions. The system first uses the Random Forest method for machine learning classifications. The study reduces non-sensitive permissions by identifying benign and harmful permission lists. Support Vector Machine classification, K-Nearest Neighbor, and Linear Regression are then used to a fresh dataset. SigPID, written in Python 3.7, is a powerful and scalable Android malware countermeasure. SigPID uses advanced machine learning and large permissions to protect the mobile ecosystem from harmful apps, making it safer and more secure.
Announcements

Call for Paper

Next Issue

Last Date : 31st Aug 2024

Search Articles

Indexed and Abstracted in

11_images.png
13_o7.png
14_o4.png
16_Open_Access_logo_with_dark_text_for_contrast,_on_transparent_background.png
17_ORCID_full_logo.png