Anomadroid: Profiling Android Applications' Behaviors for Identifying Unknown Malapps

Su, Dan; Wang, Wei; Wang, Xing; Liu, Jiqiang

doi:10.1109/trustcom.2016.0127

Cited by 14 publications

(14 citation statements)

References 19 publications

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“…Therefore, we can conclude that the top 5% of the selected features are the best features; they achieve high accuracy and save computation time. This result is consistent with results from other studies [1,7].…”

Section: Experiments For Feature Selectionsupporting

confidence: 94%

“…where iter max is the maximum number of iterations, is the current iteration of the algorithm, min is the minimum inertia weight , and is the chaotic term, as in formula (7). In the next section, we will use test environments to study the performance of the proposed fitness-based chaotic inertia weight strategy at given iterations during the course of a run.…”

Section: The Proposed Inertia Weightmentioning

confidence: 99%

“…As in most papers [5][6][7][8]20], the benign applications in our study were downloaded from the Google Play store. We chose the most popular free applications in many categories.…”

Section: Datasetmentioning

confidence: 99%

“…The works can be divided into three categories: static analysis, dynamic analysis, and hybrid analysis [1]. The analyzed features focus on permissions [2,3], APIs [4], and combined features [5][6][7][8]. When the number of features is large, feature selection methods are used to reduce the quantity and improve the performance and efficiency [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Zheng

et al. 2017

Security and Communication Networks

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Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm optimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights.

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Section: Experiments For Feature Selectionsupporting

confidence: 94%

Section: The Proposed Inertia Weightmentioning

confidence: 99%

“…As in most papers [5][6][7][8]20], the benign applications in our study were downloaded from the Google Play store. We chose the most popular free applications in many categories.…”

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Zheng

et al. 2017

Security and Communication Networks

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“…The sensor usage patterns are then used to identify whether an app has potential threat. In order to detect zero-day malapps, we developed a system called Anomadroid (anomaly Android malapp detection system) [36] that profiles the normal behaviors of apps based on only benign samples. Any app whose behaviors unacceptably deviate from the normal profile is identified as malicious.…”

Section: Background and Related Workmentioning

confidence: 99%

Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers

Wang

et al. 2018

Future Generation Computer Systems

Self Cite

191

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Android platform has dominated the markets of smart mobile devices in recent years. The number of Android applications (apps) has seen a massive surge. Unsurprisingly, Android platform has also become the primary target of attackers. The management of the explosively expansive app markets has thus become an important issue. On the one hand, it requires effectively detecting malicious applications (malapps) in order to keep the malapps out of the app market. On the other hand, it needs to automatically categorize a big number of benign apps so as to ease the management, such as correcting an app's category falsely designated by the app developer. In this work, we propose a framework to effectively and efficiently manage a big app market in terms of detecting malapps and categorizing benign apps. We extract 11 types of static features from each app to characterize the behaviors of the app, and employ the ensemble of multiple classifiers, namely, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Naive Bayes (NB), Classification and Regression Tree (CART) and Random Forest (RF), to detect malapps and to categorize benign apps. An alarm will be triggered if an app is identified as malicious. Otherwise, the benign app will be identified as a specific category. We evaluate the framework on a large app set consisting of 107,327 benign apps as well as 8,701 malapps. The experimental results show that our method achieves the accuracy of 99.39% in the detection of malapps and achieves the best accuracy of 82.93% in the categorization of benign apps.

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DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications

Yang

Wei

et al. 2016

Cluster Comput

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Anomadroid: Profiling Android Applications' Behaviors for Identifying Unknown Malapps

Cited by 14 publications

References 19 publications

Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers

DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications

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