Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)

This research synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency-Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. The TF-IDF algorithm is used to filter Android features filtered before detection process. However, IDF is unawar...

Full description

Saved in:

Bibliographic Details
Main Author:	Mazlan, Nurul Hidayah
Format:	Thesis
Language:	English English English
Published:	2019
Subjects:	QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/651/1/24p%20NURUL%20HIDAYAH%20MAZLAN.pdf http://eprints.uthm.edu.my/651/2/NURUL%20HIDAYAH%20MAZLAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/651/3/NURUL%20HIDAYAH%20MAZLAN%20WATERMARK.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uthm-ep.651
record_format	uketd_dc
spelling	my-uthm-ep.6512021-08-17T06:27:39Z Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF) 2019-02 Mazlan, Nurul Hidayah QA76 Computer software This research synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency-Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. The TF-IDF algorithm is used to filter Android features filtered before detection process. However, IDF is unaware to the training class labels and gives incorrect weight value to some features. Therefore, the proposed approach that is Modified Term Frequency – Inverse Document Frequency (MTF-IDF) algorithm give more focus on both sample and features to give correct weight value to some features. The proposed algorithm considered features based on its level of importance where weight given based on number of features involved in the sample. The related best features in the sample are selected using weight and priority ranking process using K-means. This ensures that only important malware features are selected in the Android application sample. These experiments are conducted on a sample collected from DREBIN. Comparison between existing TF-IDF algorithm and MTF-IDF algorithm have been made under various conditions such as tested on different number of sample size, different number of features used and integration of different types of features. The results showed that feature selection using MTF-IDF can improve Android malware detection analysis. It was proven that MTF-IDF is an effective Android malware detection algorithm regardless of different kinds of features or sample sizes used. MTF-IDF algorithm also proved that it can give appropriate scaling for all features in analyzing Android malware detection. 2019-02 Thesis http://eprints.uthm.edu.my/651/ http://eprints.uthm.edu.my/651/1/24p%20NURUL%20HIDAYAH%20MAZLAN.pdf text en public http://eprints.uthm.edu.my/651/2/NURUL%20HIDAYAH%20MAZLAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/651/3/NURUL%20HIDAYAH%20MAZLAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Mazlan, Nurul Hidayah Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
description	This research synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency-Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. The TF-IDF algorithm is used to filter Android features filtered before detection process. However, IDF is unaware to the training class labels and gives incorrect weight value to some features. Therefore, the proposed approach that is Modified Term Frequency – Inverse Document Frequency (MTF-IDF) algorithm give more focus on both sample and features to give correct weight value to some features. The proposed algorithm considered features based on its level of importance where weight given based on number of features involved in the sample. The related best features in the sample are selected using weight and priority ranking process using K-means. This ensures that only important malware features are selected in the Android application sample. These experiments are conducted on a sample collected from DREBIN. Comparison between existing TF-IDF algorithm and MTF-IDF algorithm have been made under various conditions such as tested on different number of sample size, different number of features used and integration of different types of features. The results showed that feature selection using MTF-IDF can improve Android malware detection analysis. It was proven that MTF-IDF is an effective Android malware detection algorithm regardless of different kinds of features or sample sizes used. MTF-IDF algorithm also proved that it can give appropriate scaling for all features in analyzing Android malware detection.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mazlan, Nurul Hidayah
author_facet	Mazlan, Nurul Hidayah
author_sort	Mazlan, Nurul Hidayah
title	Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
title_short	Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
title_full	Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
title_fullStr	Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
title_full_unstemmed	Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
title_sort	feature selection to enhance android malware detection using modified term frequency-inverse document frequency (mtf-idf)
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2019
url	http://eprints.uthm.edu.my/651/1/24p%20NURUL%20HIDAYAH%20MAZLAN.pdf http://eprints.uthm.edu.my/651/2/NURUL%20HIDAYAH%20MAZLAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/651/3/NURUL%20HIDAYAH%20MAZLAN%20WATERMARK.pdf
_version_	1747830653657808896

Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)

Similar Items