Volume 11 - Volume 11
An Enhanced Novel GA-based Malware Detection in End Systems Using Structured and Unstructured Data by Comparing Support Vector Machine and Neural Network
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.
Paper Details
PaperID: 1777
Author's Name: T. Sai Tejeshwar Reddy and A. Sivanesh Kumar
Volume: Volume 11
Issues: Volume 11
Keywords: Malware Detection, Neural Network, Support Vector Machine, Machine Learning, SPSS statistical tool, Machine Learning.
Year: 2021
Month: April
Pages: 1514-1525