SafeDroid: A Distributed Malware Detection Service for Android

Goyal, Rohit; Spognardi, Angelo; Dragoni, Nicola; Argyriou, Marios

doi:10.1109/soca.2016.14

Cited by 20 publications

(12 citation statements)

References 17 publications

(40 reference statements)

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“…For instance how the size of malware subset influence the accuracy. Although some of the related works (i.e., Safedroid [13,16], etc.) report accuracy up to 99%, we believe that our approach, relying on statistical features, can be an alternative option for detecting malware on Android.…”

Section: Resultsmentioning

confidence: 99%

“…Depending on the feature selection and the employed classifier accuracy results ranges between 85% to 97%. SafeDroid [13] provides another solution for Android malware detection that uses APIs as a feature, instead of relying on system calls, for retrofitting ML. It consists of the features extraction and the classification reporting services.…”

Section: Applications Programming Interfaces and System Callsmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Mobile Malware Detection Framework with the Support of Machine Learning

Geneiatakis

Baldini

Fovino

et al. 2018

Communications in Computer and Information Science

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Abstract. Several policies initiatives around the digital economy stress on one side the centrality of smartphones and mobile applications, and on the other call for attention on the threats to which this ecosystem is exposed to. Lately, a plethora of related works rely on machine learning algorithms to classify whether an application is malware or not, using data that can be extracted from the application itself with high accuracy. However, different parameters can influence machine learning effectiveness. Thus, in this paper we focus on validating the efficiency of such approaches in detecting malware for Android platform, and identifying the optimal characteristics that should be consolidated in any similar approach. To do so, we built a machine learning solution based on features that can be extracted by static analysis of any Android application, such as activities, services, broadcasts, receivers, intent categories, APIs, and permissions. The extracted features are analyzed using statistical analysis and machine learning algorithms. The performance of different sets of features are investigated and compared. The analysis shows that under an optimal configuration an accuracy up to 97% can be obtained.

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Section: Resultsmentioning

confidence: 99%

Section: Applications Programming Interfaces and System Callsmentioning

confidence: 99%

Towards a Mobile Malware Detection Framework with the Support of Machine Learning

Geneiatakis

Baldini

Fovino

et al. 2018

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…In the study of Kang et al, developer information was used as an attribute [21]. It was argued that the detection of malicious software can be made more effective by comparing the application certificate serial number with pre-defined malicious certificate serial numbers [21,22]. Along those lines, Utku and Dogru developed a malicious software detection system based on well-known malicious software at the application level for mobile devices [23].…”

Section: Static Analysis Methodsmentioning

confidence: 99%

Web-Based Android Malicious Software Detection and Classification System

Doğru¹,

Kiraz²

2018

Applied Sciences

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Android is the most used operating system (OS) by mobile devices. Since applications uploaded to Google Play and other stores are not analyzed comprehensively, it is not known whether the applications are malicious software or not. Therefore, there is an urgent need to analyze these applications regarding malicious software. Moreover, mobile devices have limited resources to analyze the applications. In this study, a malicious detection system named “Web-Based Android Malicious Software Detection and Classification System” was developed. The system is based on client-server architecture, static analysis and web-scraping methods. The proposed system overcomes the resource restriction issue, as well as providing third-party service support by means of client-server architecture. Based on the performance evaluation conducted in this research, the developed system’s success rate is 97.62% on benign and malicious datasets.

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“…[157] refers to the experience of Arp et al [88] in the development of Drebin. [148] Information gain (Mutual information), Chi-square statistics (CS), Fisher score (FS) [149] Information gain (Mutual information), Dependence measure [102] Information gain (Mutual information), Dependence measure [150] TF-IDF, cosine similarity [151] Sequential forward selection (SFS) [107] Fisher score (FS) [152] Dependence measure [153] Pearson correlation coefficient [154] Chi-square statistics (CS) [155] Relief algorithm [156] Genetic search (GS) [157] The selection of static features refers to the experience of Arp et al in the Drebin project.…”

Section: Data Transformationmentioning

confidence: 99%

A Review of Android Malware Detection Approaches Based on Machine Learning

et al. 2020

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Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. We believe our work complements the previous reviews by surveying a wider range of aspects of the topic. This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware. Then, taking machine learning as the focus, we analyze and summarize the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness. Finally, we assess the future prospects for research into Android malware detection based on machine learning. This review will help academics gain a full picture of Android malware detection based on machine learning. It could then serve as a basis for subsequent researchers to start new work and help to guide research in the field more generally.

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SafeDroid: A Distributed Malware Detection Service for Android

Cited by 20 publications

References 17 publications

Towards a Mobile Malware Detection Framework with the Support of Machine Learning

Towards a Mobile Malware Detection Framework with the Support of Machine Learning

Web-Based Android Malicious Software Detection and Classification System

A Review of Android Malware Detection Approaches Based on Machine Learning

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