An Enhanced Novel GA-based Malware Detection in End Systems Using Structured and Unstructured Data by Comparing Support Vector Machine and Neural Network

Authors

  • T. Sai Tejeshwar Reddy
  • A. Sivanesh Kumar

DOI:

https://doi.org/10.47059/revistageintec.v11i2.1777

Abstract

Aim: The aim of the work is to perform android malware detection using Structured and Unstructured data by comparing Neural Network algorithms and SVM. Materials and Methods: consider two groups such as Support Vector Machine and Neural Network. For each algorithm take N=10 samples from the dataset collected and perform two iterations on each algorithm to identify the Malware Detection. Result: The accuracy results of the Neural Network model has potential up to (82.91%) and the Support Vector Machine algorithm has an accuracy of (79.67%) for Android malware detection with the significance value of (p=0.007). Conclusion: classification of android malware detection using Neural Network algorithm shows better accuracy than SVM.

Downloads

Published

2021-06-04

Issue

Section

Articles