A neural network approach to category validation of Android applications

Ghorbanzadeh, Mo; Yang, Chen; Ma, Zhongmin; Clancy, T. Charles; McGwier, Robert

doi:10.1109/iccnc.2013.6504180

Cited by 35 publications

(13 citation statements)

References 20 publications

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“…According to Ghorbanzadeh, M., Chen, Y., Ma, Z., Clancy, TC and McGwier, R. [11], where they proposed to assess the security vulnerabilities seeming in a structure of permissions of an Android application. They used a dataset of 1700 benign and malicious applications in their study, they also used an Apktool tool that allows decompiling an APK file to obtain .xml files like AndroidManifest.…”

Section: Machine Learning With Static Analysis 31mentioning

confidence: 99%

Malware Analysis on Android Using Supervised Machine Learning Techniques

Rana¹,

Sung²

2018

IJCCE

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In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a new solution is enlarged while cybercriminals progress their skills to develop malware. A community including developer and researcher has been evolving substitutes aimed at refining the level of safety where numerous machine learning algorithms already been proposed or applied to classify or cluster malware including analysis techniques, frameworks, sandboxes, and systems security. One of the most promising techniques is the implementation of artificial intelligence solutions for malware analysis. In this paper, we evaluate numerous supervised machine learning algorithms by implementing a static analysis framework to make predictions for detecting malware on Android.

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Section: Machine Learning With Static Analysis 31mentioning

confidence: 99%

Malware Analysis on Android Using Supervised Machine Learning Techniques

Rana¹,

Sung²

2018

IJCCE

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“…On the contrary to the existing methods proposed in [4,[26][27][28][29]36], our employed datasets have a large number of fine-grained categories, all the utilized features in AndroClass are stable and clearly represent the actual functionalities of the app, AndroClass does not need to access the users' smartphones for feature extraction and thereby does not pose any issues to the user privacy, our method can be applied to classify unreleased or newly released apps as well, and Andro-Class does not utilize existing third-party tools for feature extraction. In Sections 2 and 3.5, we discuss these issues in detail.…”

Section: Introductionmentioning

confidence: 95%

AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features

Hamedani

Shin

Lee

et al. 2018

Wireless Communications and Mobile Computing

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Android application (app) stores contain a huge number of apps, which are manually classified based on the apps’ descriptions into various categories. However, the predefined categories or apps descriptions are usually not very accurate to reflect the real functionalities of apps, thereby leading to misclassify the apps, which may cause serious security issues and unreliability problem in the app store. Therefore, the automatic app classification is an important demand to construct a secure, reliable, integrated, and easy to navigate app store. In this paper, we propose an effective method called AndroClass to automatically classify apps based on their real functionalities by using rich and comprehensive features representing the actual functionalities of the apps. AndroClass performs three steps of feature extraction, feature refinement, and classification. In the feature extraction step, we extract 14 various features for each app by utilizing a unified tool suite. In the feature refinement step, we apply Random Forest algorithm to refine the features. In the classification step, we combine refined features into a single one and AndroClass is equipped with K-Nearest Neighbor, Naive Bayes, Support Vector Machine, and Deep Neural Network to classify apps. On the contrary to the existing methods, all the utilized features in AndroClass are stable and clearly represent the actual functionalities of the app, AndroClass does not pose any issues to the user privacy, and our method can be applied to classify unreleased or newly released apps. The results of extensive experiments with two real-world datasets and a dataset constructed by human experts demonstrate the effectiveness of AndroClass where the classification accuracy of AndroClass with the latter dataset is 83.5%.

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“…Ultimately, we evaluate the system utility at the sifted RBs and store the utility maximizing ones as feasible candidates. The RB allocation procedure (equations (7) and (8) and boundary point mapping) is illustrated in Algorithm (1) and (2).…”

Section: B Discrete Practical Rates (Rbs)mentioning

confidence: 99%

“…The situation is further compounded by the predictions estimating 3700 million subscriptions by 2017 in retrospect to only 250 million users in 2008 [1]. Moreover, smartphones' traffic diversity [2], arose from elastic (inelastic) traffic-generating delay-tolerant (real-time) applications, necessitates quality of service (QoS) requirements in order to elevate users' quality of experience (QoE), tightly bound to the subscriber churn [3]. As such, resource allocation researches attentive to the traffic diversity and dynamism have received significant interest.…”

Section: Introductionmentioning

confidence: 99%

A utility proportional fairness radio resource block allocation in cellular networks

Ghorbanzadeh

Abdelhadi

Clancy

2015

2015 International Conference on Computing, Networking and Communications (ICNC)

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This paper presents a radio resource block allocation optimization problem for cellular communications systems with users running delay-tolerant and real-time applications, generating elastic and inelastic traffic on the network and being modelled as logarithmic and sigmoidal utilities respectively. The optimization is cast under a utility proportional fairness framework aiming at maximizing the cellular systems utility whilst allocating users the resource blocks with an eye on application quality of service requirements and on the procedural temporal and computational efficiency. Ultimately, the sensitivity of the proposed modus operandi to the resource variations is investigated.

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A neural network approach to category validation of Android applications

Cited by 35 publications

References 20 publications

Malware Analysis on Android Using Supervised Machine Learning Techniques

Malware Analysis on Android Using Supervised Machine Learning Techniques

AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features

A utility proportional fairness radio resource block allocation in cellular networks

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