A Hybrid Community Based Rough Set Feature Selection Technique in Android Malware Detection

2017 ◽  
pp. 249-258 ◽  
Author(s):  
Abhishek Bhattacharya ◽  
Radha Tamal Goswami
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Malware Detection ◽  
Community Based ◽  
Feature Selection Technique ◽  
Android Malware ◽  
Selection Technique ◽  
Android Malware Detection

scholarly journals Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

2020 ◽  
Vol 07 (02) ◽  
pp. 145-159 ◽  
Author(s):  
Md. Shohel Rana ◽  
Andrew H. Sung
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Malware Detection ◽  
Learning Systems ◽  
Application Framework ◽  
Android Malware ◽  
Security Controls ◽  
Android Malware Detection ◽  
Learning Techniques

Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.

Permission-based Android Malware Detection System Using Feature Selection with Genetic Algorithm

2019 ◽  
Vol 29 (02) ◽  
pp. 245-262 ◽  
Author(s):  
Oktay Yildiz ◽  
Ibrahim Alper Doğru
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Detection System ◽  
Malware Detection ◽  
Support Vector ◽  
Android Malware ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Vector Machines ◽  
Accuracy Result

As the use of smartphones increases, Android, as a Linux-based open source mobile operating system (OS), has become the most popular mobile OS in time. Due to the widespread use of Android, malware developers mostly target Android devices and users. Malware detection systems to be developed for Android devices are important for this reason. Machine learning methods are being increasingly used for detection and analysis of Android malware. This study presents a method for detecting Android malware using feature selection with genetic algorithm (GA). Three different classifier methods with different feature subsets that were selected using GA were implemented for detecting and analyzing Android malware comparatively. A combination of Support Vector Machines and a GA yielded the best accuracy result of 98.45% with the 16 selected permissions using the dataset of 1740 samples consisting of 1119 malwares and 621 benign samples.

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