Android malware classification using optimum feature selection and ensemble machine learning

Islam, Rejwana; Sayed, Moinul Islam; Saha, Sajal; Hossain, Muhammad Shazzad; Masud, Md. Abdul

doi:10.1016/j.iotcps.2023.03.001

Cited by 25 publications

(10 citation statements)

References 18 publications

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“…This method is often called a voting classifier. Islam et al [20] presented an effective ensemble machine learning. The study showed that the weighted voting ensemble model performs better than the individual model.…”

Section: Ensemble Methodsmentioning

confidence: 99%

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Android Malware Classification Using Gain Ratio and Ensembled Machine Learning

Ansori,

Slamet,

Ghufron

et al. 2024

IJSSE

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Recently, the number of Android users has significantly increased, which has made Android a target for attackers to launch their malicious activities. Malware or malicious code is often embedded in Android apps to gain access to the user's device and retrieve personal data. Researchers have explored various approaches to mitigate the spread of Android malware. Besides, the Android malware dataset has huge dimensions with hundreds of features. Choosing the proper feature selection method is one of the challenges for producing a reliable detection model. This paper proposes an approach to detecting Android malware and classifying it into five categories using gain ratio feature selection and an ensemble machine learning algorithm. Features are reduced based on their importance value through the gain ratio calculation method. Then, features that are considered necessary are included in a classification process that combines many models. Experiment using the CICMalDroid2020 (Canadian Institute for Cybersecurity Malware of Android 2020) dataset shows that the proposed approach can improve detection performance. Gain ratio feature selection improves the detection accuracy in several machine learning classification algorithms, 2.59% in Naï ve Bayes, 0.90% in 𝑘-Nearest Neighbor, and 2.29% in Support Vector Machine. Thus, the ensembled machine learning models of Random Forest, Extra Tree, and k-Nearest Neighbors achieved the highest performance, with an accuracy of 94.57% and a precision score of 94.71%.

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Section: Ensemble Methodsmentioning

confidence: 99%

“…Dynamic analysis examines the malware by collecting memory, process, and traffic by running the application through a sandbox or designated environment [16,17]. Islam et al [20] presented a dynamic analysis technique. This study showed the impact of outlier handling when used in complex malware dataset.…”

Section: Dynamic Analysismentioning

confidence: 99%

Android Malware Classification Using Gain Ratio and Ensembled Machine Learning

Ansori,

Slamet,

Ghufron

et al. 2024

IJSSE

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“…Islam et al [6] used the CCCS-CIC-AndMal2020 dataset, with 53,439 instances and 141 features. Missing data imputation was applied with the "mean" strategy, and SMOTE was used to deal with class imbalance.…”

Section: Overview Of Machine Learning Approachesmentioning

confidence: 99%

“…ML approaches have shown to be effective and versatile in various fields, being a milestone in the tech industry. Thus, in recent years, ML techniques have been proposed for the malware detection problem in Android applications [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Explainable Machine Learning for Malware Detection on Android Applications

Palma,

Ferreira,

Figueiredo

2024

Information

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The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps.

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“…Bakır and Bakır [137] developed an autoencoder-based malware detector, but its resistance to advanced evasion remains unknown. Islam et al [136] optimized the feature selection and ensemble machine learning to classify malware. Finally, Bhat et al [165] focused on system-call-based detection by leveraging homogeneous and heterogeneous machine-learning ensembles.…”

Section: F: Malware Detection Approaches In Androidmentioning

confidence: 99%

A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms

Ferdous,

Islam,

Mahboubi

et al. 2023

IEEE Access

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The increasing sophistication of malware threats has led to growing concerns in the antimalware community, as malware poses a significant danger to online users despite the availability of numerous defense solutions. This study aims to comprehensively review malware evolution and current attack trends to identify effective defense mechanisms. It reviews the most recent journal articles, conference proceedings, reports, and online resources published during the last five years. We extensively review the malware landscape from 1970 to the present and analyze malware types, operational mechanisms, attack vectors, and vulnerabilities. Furthermore, we explore different defensive strategies developed in response to these evolving threats. Our findings highlight the increasing sophistication of malware attack trends, including a surge in cryptojacking, attacks on mobile devices, Internet of Things devices, ransomware, advanced persistent threats, supply chain attacks, fileless malware, cloud-based attacks, exploitation of remote employees, and attack trends on edge networks. Defense strategies have also evolved in parallel, emphasizing multilayered security measures to counter these dynamic threats. This study highlights the critical need for robust, multilayered security measures to combat dynamic malware. Despite these advancements, some open challenges and significant research gaps remain, which require further innovation. This review serves as a valuable guide for cybersecurity professionals by identifying the key trends, challenges, limitations, and future cybersecurity research opportunities.INDEX TERMS Malware evolution, malware attack trends, defense mechanisms, malware detection, machine learning, deep learning. D. INCREASED NUMBER OF TARGETED RANSOMWARE ATTACKSRecent malware trends have highlighted increased targeted ransomware attacks, in which hackers access a network or system, encrypt data, and demand a ransom to restore access. This increase in ransomware attacks is driven by several factors, including potential financial gains, easy operation, the rise of ransomware-as-a-service platforms, and the popularity of cryptocurrencies like Bitcoin [97]. Ransomware attacks are wide-ranging, targeting everything from desktops and mobile devices and increasingly involving IoT devices.

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Android malware classification using optimum feature selection and ensemble machine learning

Cited by 25 publications

References 18 publications

Android Malware Classification Using Gain Ratio and Ensembled Machine Learning

Android Malware Classification Using Gain Ratio and Ensembled Machine Learning

Explainable Machine Learning for Malware Detection on Android Applications

A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms

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