Malware Analysis on Android Using Supervised Machine Learning Techniques

Rana, Md. Shohel; Sung, Andrew H.

doi:10.17706/ijcce.2018.7.4.178-188

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…There have also been works that focused on comparative performance analysis of techniques in detecting malware in Android devices. Rana and Sung [32] compared the performance of individual classifiers: support vector machine (SVM), Neural networks (NN), naïve bayes (NB), DT, Linear Discriminant Analysis (LDA) and k Nearest Neighbor (KNN) in detecting Android malware. KNN was found to perform better than the rest.…”

Section: Related Workmentioning

confidence: 99%

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Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Akintola¹,

Balogun

Mojeed³

et al. 2022

Int. J. Interact. Mob. Technol.

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Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for identifying and categorising malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognising malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, a synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to other experimented methods. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection.

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Section: Related Workmentioning

confidence: 99%

“…During the experimental section of this research, two Android malware datasets were employed. These datasets are widely available and often utilized in current research [6,32,[45][46][47]. The first dataset (Drebin) consists of 15,036 instances (5,560 malware and 9476 benign).…”

Section: Android Malware Datasetsmentioning

confidence: 99%

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Akintola¹,

Balogun

Mojeed³

et al. 2022

Int. J. Interact. Mob. Technol.

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Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection

Rana

Gudla

Sung

2019

Advances in Intelligent Systems and Computing

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Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

Rana

Sung

2020

Vietnam J. Comp. Sci.

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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.

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Malware Analysis on Android Using Supervised Machine Learning Techniques

Cited by 6 publications

References 10 publications

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

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