Monitoring Smartphones for Anomaly Detection

Schmidt, Aubrey-Derrick; Peters, Frank; Lamour, Florian; Scheel, Christian; Camtepe, Seyit; Albayrak, Şahin

doi:10.1007/s11036-008-0113-x

Cited by 94 publications

(42 citation statements)

References 10 publications

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“…A monitor of Symbian OS and Windows Mobile smartphone that extracts features for anomaly detection has, for example, been used to monitor logs and detect normal and infected traffic in [21]. However, this system does not run on a mobile device itself.…”

Section: General Mobile Malware Detection Techniquesmentioning

confidence: 99%

“…According to Cisco, 497 million new mobile devices and connections were sold in 2014 [9]. Other recent reports forecast that mobile-cellular subscriptions will be more than 7bn by the end of 2015 [21]. Market reports also show that since 2012 Google's Android operating system has overtaken other smartphone operating systems and accounted for more than 80% share of market in 2014.…”

Section: Introductionmentioning

confidence: 99%

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MBotCS: A Mobile Botnet Detection System Based on Machine Learning

Meng

Spanoudakis

2016

Lecture Notes in Computer Science

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract. As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices. Permanent repository link

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Section: General Mobile Malware Detection Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MBotCS: A Mobile Botnet Detection System Based on Machine Learning

Meng

Spanoudakis

2016

Lecture Notes in Computer Science

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“…[30] includes framework that consist of a monitoring client, Remote Anomaly Detection System (RADS) and a visualization component in order to monitor smartphones to extract features that can be used in a machine learning algorithm to detect anomalies. A behavior-based malware detection system (pBMDS) is proposed in [31] that use correlation between user's inputs and system calls in order to detect anomalous activities related to SMS/MMS sending. A new service named Kirin security service for Android is described in [33] and [34] that perform lightweight certification of applications to mitigate malware at install time.…”

Section: Malware Detection In Smartphonementioning

confidence: 99%

Android Mobile Platform Security and Malware Survey

.¹

2013

IJRET

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“…To prevent the rapid depletion of the power source, the monitored data is sent in bulks. The Feature Extractor is triggered to fetch new data every thirty seconds which is stored locally and later, upon reaching a threshold, sent to the server using the appropriate webservice [23]. This data consists of system characteristics that describe all areas of the monitored device.…”

Section: Smartphone System Data Set and Experimentsmentioning

confidence: 99%

“…A detailed survey of the field can be found in [14]. Recently, malicious software detection in smartphones and mobile devices has been a topic of increasing interest [27,7,23,21].…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Diffusion Scheme for Anomaly Detection on Smartphones

Alpcan

Bauckhage

Schmidt

2010

Information Security Theory and Practices. Security and Privacy of Pervasive Systems and Smart Devices

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Abstract. Widespread use and general purpose computing capabilities of next generation smartphones make them the next big targets of malicious software (malware) and security attacks. Given the battery, computing power, and bandwidth limitations inherent to such mobile devices, detection of malware on them is a research challenge that requires a different approach than the ones used for desktop/laptop computing. We present a novel probabilistic diffusion scheme for detecting anomalies possibly indicating malware which is based on device usage patterns. The relationship between samples of normal behavior and their features are modeled through a bipartite graph which constitutes the basis for the stochastic diffusion process. Subsequently, we establish an indirect similarity measure among sample points. The diffusion kernel derived over the feature space together with the Kullback-Leibler divergence over the sample space provide an anomaly detection algorithm. We demonstrate its applicability in two settings using real world mobile phone data. Initial experiments indicate that the diffusion algorithm outperforms others even under limited training data availability.

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Monitoring Smartphones for Anomaly Detection

Cited by 94 publications

References 10 publications

MBotCS: A Mobile Botnet Detection System Based on Machine Learning

MBotCS: A Mobile Botnet Detection System Based on Machine Learning

Android Mobile Platform Security and Malware Survey

A Probabilistic Diffusion Scheme for Anomaly Detection on Smartphones

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