Android Malware Detection Using Complex-Flows

Shen, Feng; Vecchio, Justin Del; Mohaisen, Aziz; Ko, Steven Y.; Ziarek, Lukasz

doi:10.1109/icdcs.2017.190

Cited by 20 publications

(3 citation statements)

References 19 publications

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“…Unfortunately, this approach cannot detect compressed or encrypted code. • Complex-flow analysis is a new type of information flow analysis proposed by Shen et al [28], which ac- cording to the authors, can produce 86.5% detection rate. Unfortunately, their method cannot detect malicious behavior that is present in native code, which is the case for some of the latest mobile malware.…”

Section: Discussionmentioning

confidence: 84%

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A Survey on Mobile Malware Detection Techniques

Kouliaridis

Barmpatsalou

Kambourakis

et al. 2020

IEICE Trans. Inf. & Syst.

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Modern mobile devices are equipped with a variety of tools and services, and handle increasing amounts of sensitive information. In the same trend, the number of vulnerabilities exploiting mobile devices are also augmented on a daily basis and, undoubtedly, popular mobile platforms, such as Android and iOS, represent an alluring target for malware writers. While researchers strive to find alternative detection approaches to fight against mobile malware, recent reports exhibit an alarming increase in mobile malware exploiting victims to create revenues, climbing towards a billion-dollar industry. Current approaches to mobile malware analysis and detection cannot always keep up with future malware sophistication [2], [4]. The aim of this work is to provide a structured and comprehensive overview of the latest research on mobile malware detection techniques and pinpoint their benefits and limitations.

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Section: Discussionmentioning

confidence: 84%

“…Shen et al [28] proposed a malware detection approach based on information flow analysis. The authors proposed complex-flow as a new representation schema for information flows.…”

Section: Anomaly-based Detectionmentioning

confidence: 99%

A Survey on Mobile Malware Detection Techniques

Kouliaridis

Barmpatsalou

Kambourakis

et al. 2020

IEICE Trans. Inf. & Syst.

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“…A mobile malware detection scheme that relies on information flow analysis has been suggested by Shen et al [31]. They introduced the notion of complex-flow as a new representation approach for information flows.…”

Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

Kouliaridis

Kambourakis

2021

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Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field.

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