An Android Malware Detection Framework-based on Permissions and Intents

Verma, Sushma; Muttoo, Sunil Kumar

doi:10.14429/dsj.66.10803

Cited by 27 publications

(15 citation statements)

References 5 publications

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“…After that, machine learning classification algorithms have been used for classifying the apps to multiple classes. Moreover, in [114], a hybrid classification method has been proposed to classify Android apps using k-means algorithm as a clustering phase followed by the J48 and ID 3 classifiers as a classifying phase. Also, the K-means clustering algorithm has been used with some classification algorithms in [78].…”

Section: Clustering Algorithms This Type Of Algorithms Ismentioning

confidence: 99%

“…It is worth noted that, we classified the works which use pattern matching method such as [52,118] under the signature-based works. Moreover, the 'Combination' sub section in the machine learning section indicates to the works which used both of supervised and unsupervised machine learning algorithms, such as [113,114].…”

Section: Clustering Algorithms This Type Of Algorithms Ismentioning

confidence: 99%

“…This algorithm depends on calculating the entropy values of the features and selecting the highest gain features to be used in training the classification model. This algorithm is the most used algorithm in the studied works, such as in [48,95,111,113,114]. In [119], the features have been ranked using mutual information method to select the top 10, 15, 20 and 25 features.…”

Section: Feature Selection Phasementioning

confidence: 99%

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The Android malware detection systems between hope and reality

2019

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The widespread use of Android-based smartphones made it an important target for malicious applications' developers. So, a large number of frameworks have been proposed to tackle the huge number of daily published malwares. Despite there are many review papers that have been conducted in order to shed light on the works that achieved in Android malware analysing domain, the number of conducted review papers do not fit with the importance of this research field and with the volume of achieved works. Also, there is no comprehensive taxonomy for all research trends in the field of analysing malicious applications targeting the Android system. Furthermore, none of the existing review papers contains a schematic model that makes it easy for the reader to know the methods and methodologies used in a particular field of research without much effort. This paper aims at proposing a comprehensive taxonomy and suggesting a new schematic review approach. To this end, a review of a large number of works that achieved between 2009 and 2019 has been conducted. The achieved study includes more than 200 papers that have different goals such as apps' behaviour analysis, automatic user interface triggers or packer/unpacker frameworks development. Also, a comprehensive taxonomy has been proposed so that most of the previous works can be classified under it. To the best of our knowledge, the suggested taxonomy is the widest and the most comprehensive in terms of the covered research trends. Moreover, we have proposed a detailed schematic model (called Schematic Review Model) illustrates the process of detecting the malignant applications of an Android in the light of the studied works and the proposed taxonomy. To our knowledge, this is the first time that the Android malware detection methods have been explained in this way with this amount of detail. Furthermore, the studied researches have been analysed according to multiple criteria such as used analysing method, used features, used detection method, and used dataset. Also, the features used in the studied works were discussed in detail by dividing it into multiple classes. Moreover, the challenges facing Android's malware analysing methods were discussed in detail. Finally, it has been concluded that there are gaps between the size and the goal of the conducted works and the number of malicious apps published every day, so some future works areas have been proposed and discussed.

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Section: Clustering Algorithms This Type Of Algorithms Ismentioning

confidence: 99%

Section: Clustering Algorithms This Type Of Algorithms Ismentioning

confidence: 99%

Section: Feature Selection Phasementioning

confidence: 99%

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The Android malware detection systems between hope and reality

2019

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“…The increased usage of smart phones can be combined with tremendous increase in the security breaches due to the exploitation of the increasing number of android application related vulnerabilities [1]. Faruki et al [2] presented an overview on the current malware trends. Malware analysis generally uses static, dynamic, and hybrid methods for analysis.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method of Android Malware Detection Based on Ensemble Learning Algorithm

2018

Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering

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Android is popular for mobile devices in recent years. As the risk of malware is sharply increasing in Android platform, Android malware detection has become an important research topic. However, the current work shows that the detection of malware still needs to be reformed. Many current results suggest a modification of permissions or a combination of permissions and intents, but Android's fragmentation issues and requiring rooting, hindering the widespread adoption of those methods. Many approaches have been proposed to detect this attack by modifying the Android OS. Existing anti-viruses depend on signature databases that need to be updated from time to time and are unable to detect zero-day malware. The Android Operating system allows inter-application communication through the use of component reuse by using intents. A malware detection model is developed based on ensemble learning algorithms and on the basis of the random forest in WEKA. The method we proposed can take a better efficiency and precision.

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“…The growth of android application malware has been made possible by the free application market for the Android OS [9]. The most severe and important threat to the Android OS arises as a result of malicious applications especially from unauthorized applications market that can easily be installed on any phone [9]. Android applications depend on users to grant permission to work optimally.…”

Section: Introductionmentioning

confidence: 99%