Android mobile malware detection using Fuzzy AHP

QR Code

Android mobile malware detection using Fuzzy AHP

Android mobile is very challenging because it is an open-source operating system that is also vulnerable to attacks. Previous studies have shown various mobile malware detection methods to overcome this problem, but still, there is room for improvement. Mobile users mostly ignore long lists of permi...

Full description

Bibliographic Details
Main Authors:	Juliza, Mohamad Arif, Mohd Faizal, Ab Razak, Sharfah Ratibah, Tuan Mat, Suryanti, Awang, Nor Syahidatul Nadiah, Ismail, Ahmad Firdaus, Zainal Abidin
Format:	Article
Language:	English
Published:	Elsevier 2021
Subjects:	QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/31625/ http://umpir.ump.edu.my/id/eprint/31625/7/Android%20Mobile%20Malware%20Detection%20Using%20Fuzzy%20AHP-1.pdf

Similar Items

Android mobile malware detection system using fuzzy AHP
by: Juliza, Mohamad Arif, et al.
Published: (2021)

A review : Static analysis of android malware and detection technique
by: Juliza, Mohamad Arif, et al.
Published: (2021)

A static analysis approach for android permission-based malware detection systems
by: Juliza, Mohamad Arif, et al.
Published: (2021)

A Bayesian probability model for Android malware detection
by: Sharfah Ratibah, Tuan Mat, et al.
Published: (2021)

Android mobile malware detection model based on permission features using machine learning approach
by: Sharfah Ratibah, Tuan Mat
Published: (2022)

Applying Bayesian probability for Android malware detection using permission features
by: Sharfah Ratibah, Tuan Mat, et al.
Published: (2021)

Fuzzy analytical hierarchy process based risk assessment for malware detection in android mobile system
by: Juliza, Mohamad Arif
Published: (2022)

Towards a systematic description of the field using bibliometric analysis: malware evolution
by: Sharfah Ratibah, Tuan Mat, et al.
Published: (2021)

Android: S-Based Technique in Mobile Malware Detection
by: Rahiwan Nazar, Romli, et al.
Published: (2018)

Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA)
by: Nur Khairani, Kamarudin, et al.
Published: (2023)

Techniques for analysing android malware
by: Adebayo, Olawale Surajudeen, et al.
Published: (2014)

Understanding the root of attack in android malware
by: Rahiwan Nazar, Romli, et al.
Published: (2018)

Machine Learning Malware Detection For Android
by: Amir Muhammad Hafiz, Othman
Published: (2022)

Malware Detection In Android Using Machine Learning
by: Muhammad Hazriq Akmal, Zairol
Published: (2023)

Discovering optimal features using static analysis and a genetic search based method for Android malware detection
by: Ahmad Firdaus, Zainal Abidin, et al.
Published: (2018)

Malware detection using static analysis in android: A review of FeCO (features, classification, and obfuscation)
by: Rosmalissa, Jusoh, et al.
Published: (2021)

The Rise of “malware”: Bibliometric Analysis of Malware Study
by: Mohd Faizal, Ab Razak, et al.
Published: (2016)

Automated feature selection using boruta algorithm to detect mobile malware
by: Che Akmal, Che Yahaya, et al.
Published: (2020)

Towards a systematic description of the feld using bibliometric analysis
by: Sharfah Ratibah, Tuan Mat, et al.
Published: (2021)

Bio-inspired for Features Optimization and Malware Detection
by: Mohd Faizal, Ab Razak, et al.
Published: (2018)

Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
by: Adebayo, Olawale Surajudeen, et al.
Published: (2014)

The trend of mobile malwares and effective detection techniques
by: Abdul Aziz, Normaziah, et al.
Published: (2015)

Mobile malware anomaly-based detection systems using static analysis features / Ahmad Firdaus Zainal Abidin
by: Ahmad Firdaus, Zainal Abidin
Published: (2017)

Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization
by: Adebayo, Olawale Surajudeen, et al.
Published: (2015)

Malaysia Blood Bank Android Mobile Application
by: Lim, Yi Ci
Published: (2023)

Features, Analysis Techniques, and Detection Methods of Cryptojacking Malware: A Survey
by: Kadhum, Laith M., et al.
Published: (2024)

A malware risk analysis and detection system for mobile devices using permission-based features / Mohd Faizal Ab Razak
by: Mohd Faizal , Ab Razak
Published: (2018)

Dawn Of The Mobile Malware: Reviewing Mobile Worms .
by: Sarwar, Usman, et al.
Published: (2007)

Deep learning based hybrid analysis of malware detection and classification: A recent review
by: Hussain, Syed Shuja, et al.
Published: (2023)

Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
by: Mazlan, Nurul Hidayah
Published: (2019)

CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning
by: Nur Khairani, Kamarudin, et al.
Published: (2023)

Malware Analysis with Multiple Features
by: Ahmad Zabidi, Muhammad Najmi, et al.
Published: (2012)

The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware
by: Nur Khairani, Kamarudin, et al.
Published: (2024)

Malware visualizer: A web apps malware family classification with machine learning
by: Mohd Zamri, Osman, et al.
Published: (2021)

Kiki: A Chatbot Application For OCD In Android Mobile Application
by: Kong, Kei
Published: (2023)

Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
by: Ahmad, Firdaus, et al.
Published: (2017)

A malware analysis and detection system for mobile devices / Ali Feizollah
by: Ali, Feizollah
Published: (2017)

Easy-Z notes mobile application in android application (GPS Feature)
by: Siti Zulaikha, Azman
Published: (2019)

Malware detection system using cloud sandbox, machine learning
by: Mail, Mohd Azuwan Efendy, et al.
Published: (2022)

“Less Give More”: Evaluate and zoning Android applications
by: Mohd Faizal, Ab Razak, et al.
Published: (2019)