Exposing mobile malware from the inside (or what is your mobile app really doing?)

Damopoulos, Dimitrios; Kambourakis, Georgios; Gritzalis, Stefanos; Park, Sang Oh

doi:10.1007/s12083-012-0179-x

Cited by 22 publications

(10 citation statements)

References 7 publications

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“…those that generate considerable network traffic. • iOS Detection approaches, such as the work proposed by Damopoulos et al [30], [31], produce high accuracy results, however these approaches require jailbreaking [40], which could put the device at risk and make the end-user reluctant to employ it. • Hanlin et al [36] use sandboxing to safely analyze malware behavior.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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: 99%

“…Damopoulos et al [30] proposed a tool which dynamically analyzes iOS apps in terms of method invocation. The authors designed and implemented an automated malware analyzer and detector for the iOS platform, namely iDMA.…”

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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“…Specifically, we cross-evaluate four supervised machine learning algorithms, i.e., Bayesian Networks, Radial Basis function (RBF), K-Nearest Neighbor (KNN) and Random Forest [41]. The data analysis was carried out using Weka.…”

Section: System Evaluationmentioning

confidence: 99%

Europe in the shadow of financial crisis: Policy Making via Stance Classification

Spiliotopoulou

Damopoulos

Charalabidis

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…The First effort to recognize and examine the malware on the mobile phones introduced by existing PC Security Solution but this was not the reasonable solution Schmidt is one the person who suggested mobile malware detection for Android Smartphone [14] . This proposed algorithm derive function calls from binaries of application and for detecting unrevealed malware a clustering mechanism called Centroid is used .Which is achieved by implementing static analysis of Executable and Linking Format(ELF) objects files by utilizing command readelf in Android.…”

Section: Related Studies On Mobile Malware Detectionmentioning

confidence: 99%