Android malware detection applying feature selection techniques and machine learning

Keyvanpour, Mohammad Reza; Shirzad, Mehrnoush Barani; Heydarian, Farideh

doi:10.1007/s11042-022-13767-2

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…The AdMat [18] had 17626 features from the 23000 features (76%). The EF-RF [19] had selected the 11381 features from 123453 (9%). In this proposed method, the AWGWO selected 172 features from the 216 features (80%).…”

Section: Discussionmentioning

confidence: 99%

“…Mohammad Reza Keyvanpour [19] presented the Effective Feature selection -Random Forest (EF-RF) approach-based feature selection for the detection of malware in Android. The suggested approach was utilized for the three various feature selection methods such as maximum weight, effective, as well as effective group selection of the feature, which can be applied in pre-processing.…”

Section: Literature Surveymentioning

confidence: 99%

“…After, subsequent adaptive weight factors can be developed based on the current divergent distance change rate of every individual concerning α, β, and δ is expressed in Eqs. ( 17)- (19) as follows;…”

Section: Adaptive Weight Factor-based Gwomentioning

confidence: 99%

“…Tables 4 and 5 compare the proposed AGWO-SVM method to a collection of existing algorithms using the Drebin and CICInvesAndMal2019 dataset. The existing methods such as [16][17][18][19][22][23][24] are compared by using accuracy, precision, recall, F1score and FPR respectively.…”

Section: Comparative Analysismentioning

confidence: 99%

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Feature Selection Using Adaptive Weight Based Grey Wolf Optimization for Malware Detection in Android

2024

IJIES

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The malware application is one of the most dangerous threats to various applications, particularly in Android devices. To effectively detect the malware in Android, the existing researchers utilized the Machine Learning (ML) approaches. However, the researchers have not accurately detected the malware due to the malware complexity, continuous changes as well as increased damages caused by attackers. In this paper, an Adaptive Weight based Grey Wolf Optimization (AWGWO) based feature selection is proposed for malware detection in Android. The proposed AWGWO is evaluated by using Drebin and CICInvesAndMal2019 dataset which contains 216 and 428 malware features. Then, the acquired dataset is pre-processed by utilizing the Min-Max normalization technique to remove the computational complexity and achieve minimum error rates. In the classification stage, a voting combination of benign and malignant malware is used while utilizing three ensemble ML approaches. The ensemble approaches involve a Support Vector Machine (SVM), Random Forest (RF) and AdaBoost algorithm. The proposed AWGWO method achieves better results by using evaluation metrics like accuracy, precision, recall, and F1-score of 0.9953, 0.9930, 0.9963, and 0.9725 respectively which is comparatively better than the existing methods named Random Forest-OWL (RF-OWL), SVM, Adjacency Matrix (AdMat) and Effective Feature selection -RF (EF-RF).

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Section: Discussionmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Adaptive Weight Factor-based Gwomentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

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Feature Selection Using Adaptive Weight Based Grey Wolf Optimization for Malware Detection in Android

2024

IJIES

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“…In decision tree classification, considering the characteristics of features affects the decision on node splitting (Keyvanpour et al, 2023). The root is the attribute that includes the most data (Singh & Singh, 2021).…”

Section: Decision Treementioning

confidence: 99%

A review of detecting malware in android devices based on machine learning techniques

Sharma,

Kaul

2023

Expert Systems

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Malware developers install malware on mobile users' devices and steal their personal information without their knowledge. According to recent studies, it has been observed that malware developers are now targeting Android mobile devices. Researchers have examined the issues of detecting malware in these devices and proposed different methods and techniques. This study's main goal is to aid researchers in gaining a basic understanding of Android malware and its numerous detection methods. Earlier experiments that used machine learning to detect Android malware will be carefully reviewed in this paper. This in‐depth review article thoroughly examines the origins, evolution, and sustainability of Android malware detection. It offers an in‐depth literature review that includes the most recent approaches and research trends for detecting malware, from static analysis to dynamic analysis, machine learning, and deep learning. Additionally, we review current approaches' shortcomings and difficulties and suggest possible paths for further investigation. The paper aims to stimulate further innovation in this essential field by providing researchers and practitioners with a comprehensive overview of the current status of Android malware detection.

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Security Defect Identification of Android Applications by Permission Extraction using Machine Learning

Kumar,

Singh

2023

Communications in Computer and Information Science

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Android malware detection applying feature selection techniques and machine learning

Cited by 13 publications

References 32 publications

Feature Selection Using Adaptive Weight Based Grey Wolf Optimization for Malware Detection in Android

Feature Selection Using Adaptive Weight Based Grey Wolf Optimization for Malware Detection in Android

A review of detecting malware in android devices based on machine learning techniques

Security Defect Identification of Android Applications by Permission Extraction using Machine Learning

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