A Comparison of Features for Android Malware Detection

Leeds, Matthew; Keffeler, Miclain; Atkison, Travis

doi:10.1145/3077286.3077288

Cited by 21 publications

(11 citation statements)

References 10 publications

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“…Their article analysed two major characteristics for Android malware detection, permission and system calls, and applied machinery education to both. The results suggested that permission data is better for malware detection than system call data [79].…”

Section: Fig 2 Architecture Of Droid Deep [73]mentioning

confidence: 99%

A Comprehensive Study of Malware Detection in Android Operating Systems

Hamdi

Ibrahim

Omar

et al. 2021

AJRCoS

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Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static.

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Section: Fig 2 Architecture Of Droid Deep [73]mentioning

confidence: 99%

A Comprehensive Study of Malware Detection in Android Operating Systems

Hamdi

Ibrahim

Omar

et al. 2021

AJRCoS

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“…The purpose of this method was to verify whether the app's benefits outweighed its expected risks. Some studies [3], [29], [37]- [39], [41], [44], [46], [77], [83], [196], [214], [216], [233], [235], [238] extracted permissions as well as some other features and utilized machine learning to detect malapps. This approach usually achieved accuracy as more than 94%.…”

Section: ) Permissionmentioning

confidence: 99%

“…The experimental results demonstrated that their approach possessed the malapp detection ability with high accuracy. Leeds et al [214] compared static and dynamic analysis of Android apps. They extracted permissions requested at install-time as static features for static analysis.…”

Section: ) System Callmentioning

confidence: 99%

Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions

et al. 2019

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The number of applications (apps) available for smart devices or Android based IoT (Internet of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill-designed or malicious apps (malapps) has been growing explosively. To ensure the quality and security of the apps in the markets, many approaches have been proposed in recent years to discriminate malapps from benign ones. Machine learning is usually utilized in classification process. Accurately characterizing apps' behaviors, or so-called features, directly affects the detection results with machine learning algorithms. Android apps evolve very fast. The size of current apps has become increasingly large and the behaviors of apps have become increasingly complicated. The extracting effective and representative features from apps is thus an ongoing challenge. Although many types of features have been extracted in existing work, to the best of our knowledge, no work has systematically surveyed the features constructed for detecting Android malapps. In this paper, we are motivated to provide a clear and comprehensive survey of the state-of-the-art work that detects malapps by characterizing behaviors of apps with various types of features. Through the designed criteria, we collect a total of 1947 papers in which 236 papers are used for the survey with four dimensions: the features extracted, the feature selection methods employed if any, the detection methods used, and the scale of evaluation performed. Based on our in-depth survey, we highlight the issues of exploring effective features from apps, provide the taxonomy of these features and indicate the future directions.

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“…The features examination for Android malware detection is also researched in [16], by considering the Java API call data as features sources for malware detection. The extracted features are weighted in order to provide some reliable indicators of the malicious potential of the source data or Android app.…”

Section: Related Workmentioning

confidence: 99%

“…The hyperparameters are the higher-level properties of the model that are chosen by the user. Other typical examples of hyperparameters are: the decay rate of the learning rate, the number of training steps, the size of training dataset [16].…”

Section: Related Workmentioning

confidence: 99%

Android Malware Detection and Crypto-Mining Recognition Methodology with Machine Learning

Soviany¹,

Scheianu²,

Suciu

et al. 2018

2018 IEEE 16th International Conference on Embedded and Ubiquitous Computing (EUC)

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The paper proposes a Machine Learning methodology for Android malware detection and recognition, including crypto-mining applications using the blockchain. The design is based on a hierarchical classification method, with several decision stages. A combination of functional and statistical features is proposed to be applied for data classification in order to provide a high-performance malware recognition process. The specific contribution of this design methodology is the hierarchical classifier with detection and discrimination stages, respectively. Further works should be done for various features sets in order to achieve an optimized and high-accuracy modeling process supporting an innovative Machine Learning-based solution for Android malware detection.

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A Comparison of Features for Android Malware Detection

Cited by 21 publications

References 10 publications

A Comprehensive Study of Malware Detection in Android Operating Systems

A Comprehensive Study of Malware Detection in Android Operating Systems

Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions

Android Malware Detection and Crypto-Mining Recognition Methodology with Machine Learning

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