MobSafe: cloud computing based forensic analysis for massive mobile applications using data mining

Xu, Jianzhong; Yu, Yifan; Chen, Zhen; Cao, Bin; Dong, Wenyu; Guo, Yu; Cao, Junwei

doi:10.1109/tst.2013.6574680

Cited by 32 publications

(9 citation statements)

References 14 publications

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“…Note that both a benign and a malicious app may require the same permissions and are thus indistinguishable via this permission-based mechanism. In general, permission-based approaches are developed primarily for risk assessment [7][8][9][10] rather than malware detection.…”

Section: Introductionmentioning

confidence: 99%

Droiddetector: android malware characterization and detection using deep learning

Yuan

Lü

Xue

2016

Tinshhua Sci. Technol.

383

158

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Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine (DroidDetector) that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test DroidDetector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. DroidDetector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.

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Section: Introductionmentioning

confidence: 99%

Droiddetector: android malware characterization and detection using deep learning

Yuan

Lü

Xue

2016

Tinshhua Sci. Technol.

383

158

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“…While Jianlin Xu [19] created a framework named MobSafe. The target of this framework is to distinguish and quantifythe portable application that is benevolent.…”

Section: Comparative Study On Existing Workmentioning

confidence: 99%

Understanding the Root of Attack in Android Malware

Romli

Zolkipli

Almaarif³

et al. 2018

IJIE

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With the rapid pace of development and change in mobile device technology and Android versions, Android malware has emerged and become a focus in current research. Subsequently, security and privacy have become one of the main issues in android malware. Therefore, it is essential to understand the behavior of Android malware in order to conceive an effective technique in malware detection and analysis. This article presents a comprehensive study regarding Android platform, its features in android malware code and also discusses the result from previous studies in order to support forward-looking in Android study.

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“…Hadoop is widely used in applicat ions where large scale data processing is necessary. Customer feedback analysis [36], social med ia analysis [38], ad-targeting [39], data warehouse modernization [34], operation analysis [35], fraud detection [37]…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Novel Big Data Approach to Classify Bank Customers - Solution by Combining PIG, R and Hadoop

Mohan¹,

Elayidom²

2016

IJITCS

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Large amount of data that is characterized by its volu me, velocity, veracity, value and variety is termed Big Data. Extracting hidden patterns, customer preferences, market trends, unknown correlations, or any other useful business information fro m large collection of structured or unstructured data set is called Big Data analysis. This article exp lores the scope of analyzin g bank transaction data to categorize customers wh ich could help the bank in efficient marketing, improved customer service, better operational efficiency, increased profit and many other hidden benefits. Instead of relying on a single technology to process large scale data, we make use of a co mbination of strategies like Hadoop, PIG, R etc for efficient analysis. RHadoop is an upcoming research trend for Big Data analysis, as R is a very efficient and easy to code, data analysis and visualizat ion tool compared to tradit ional Map Reduce program. K-Means is chosen as the clustering algorith m for classification.

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MobSafe: cloud computing based forensic analysis for massive mobile applications using data mining

Cited by 32 publications

References 14 publications

Droiddetector: android malware characterization and detection using deep learning

Droiddetector: android malware characterization and detection using deep learning

Understanding the Root of Attack in Android Malware

A Novel Big Data Approach to Classify Bank Customers - Solution by Combining PIG, R and Hadoop

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