An Enhanced Novel GA-based Malware Detection in End Systems Using Structured and Unstructured Data by Comparing Support Vector Machine and Neural Network
2021 ◽
Vol 11
(2)
◽
pp. 1514-1525
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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.
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2021 ◽
Vol 9
(8)
◽
pp. 2284-2288
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