Using Weighted Based Feature Selection Technique for Android Malware Detection

2017 ◽  
pp. 54-64
Author(s):  
Nurul Hidayah Mazlan ◽  
Isredza Rahmi A. Hamid
Keyword(s):  
Feature Selection ◽  
Malware Detection ◽  
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.

scholarly journals Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2813
Author(s):  
Jaehyeong Lee ◽  
Hyuk Jang ◽  
Sungmin Ha ◽  
Yourim Yoon
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection

Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.

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