CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning

QR Code

CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning

Today many smart devices are running on Android systems. With the increasing popularity of Android, mobile malware continuously evolves as well, and further attacks Android operating systems. To address these shortcoming issues many security experts use different approaches to detect malware based o...

Full description

Bibliographic Details
Main Authors:	Nur Khairani, Kamarudin, Ahmad Firdaus, Zainal Abidin, Azlee, Zabidi, Mohd Faizal, Ab Razak
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/40367/ http://umpir.ump.edu.my/id/eprint/40367/1/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202.pdf http://umpir.ump.edu.my/id/eprint/40367/2/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202%20with%20strong%20gravity%20call%20graph%20and%20deep%20learning_ABS.pdf

Similar Items

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

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

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

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

Cloud of Word vs DroidKungfu: Performance evaluation in detecting root exploit malware with deep learning approach
by: Che Akmal, Che Yahaya, et al.
Published: (2023)

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

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

An artificial co-stimulation classifier for malicious API calls classification in portable executable malwares / Saman Mirza Abdulla
by: Abdulla, Saman Mirza
Published: (2012)

Ensemble based categorization and adaptive model for malware detection
by: Ahmad Zabidi, Muhammad Najmi, et al.
Published: (2011)

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

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

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

Maldroid- attribute selection analysis for malware classification
by: Rahiwan Nazar, Romli, et al.
Published: (2019)

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

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

Malware detection through machine learning techniques
by: Amer, Ahmed, et al.
Published: (2019)

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

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

Malware classification based on target location
by: Nasuha, Noor Baha
Published: (2014)

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

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

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

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

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

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

A framework for malware identification based on behavior
by: Mohamad Fadli, Zolkipli
Published: (2012)

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

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

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

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

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)

Efficient feature selection analysis for accuracy malware classification
by: Rahiwan Nazar, Romli, et al.
Published: (2021)

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

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)

Power Dominating Numbers In Graphs
by: Chan, Kar Man
Published: (2021)

Malware detection using n-gram with TF-IDF weighting
by: Natasha, Zainal
Published: (2018)

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

A Study on Graphs of Rings
by: Lau, Zhou Sheng
Published: (2019)

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

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