An Android Malware Detection Approach Using Community Structures of Weighted Function Call Graphs

Du, Yao; Wang, Junfeng; Li, Qi

doi:10.1109/access.2017.2720160

Cited by 42 publications

(18 citation statements)

References 27 publications

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“…Finally, a machine learning classification process is used to detect malware. In an evaluation of 13,790 Android applications, our method achieves 96.5 accuracy in detecting unknown malware [20]. Though the method achieves good malware detection performance, the experimental results indicate that the runtime performance of our method could be improved for large function call graphs with more computational resources.…”

Section: An Android Malware Detection Approach Using Community Strmentioning

confidence: 90%

Exterminating Computational Limits of Machine Learning with Merits of Serverless

Kaur¹,

Kaur²

2018

IJERT

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-The trend of determining patterns using the various machine-learning, data mining, deep-learning or neural networks are limited due to the computational power of machines. Sometimes the cost estimation exceeds heavily from the estimates, or the cost is not sufficient for the enterprises to get the desired infrastructure. Unexpected computational resources at times limit the accuracy and increase the cost of infrastructure to find the perfect patterns. A serverless compute may give an optimal solution to the computational resources provided by the cloud. Resources are elastic, which can expand and shrink according to the input data sets, resulting in lesser computational costs. Index Terms -AWS (Amazon

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Section: An Android Malware Detection Approach Using Community Strmentioning

confidence: 90%

Exterminating Computational Limits of Machine Learning with Merits of Serverless

Kaur¹,

Kaur²

2018

IJERT

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“…Although dynamic detections are effective in identifying malicious behaviors, they require a lot of costs [25]. Meanwhile, for conditionally triggered malicious applications, dynamic detections are also helpless.…”

Section: Dynamic Detectionmentioning

confidence: 99%

“…However, machine learning has high requirements for sample features. Now, more and more scholars use hybrid methods to detect Android malware [25,[30][31][32]. Y.…”

Section: Hybrid Detectionmentioning

confidence: 99%

A detection method for android application security based on TF-IDF and machine learning

et al. 2020

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Android is the most widely used mobile operating system (OS). A large number of thirdparty Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware detection techniques. However, due to improvements of malware itself and Android system, it is difficult to design a detection method that can efficiently and effectively detect malicious apps for a long time. Meanwhile, adopting more features will increase the complexity of the model and the computational cost of the system. Permissions play a vital role in the security of the Android apps. Term Frequency-Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus. The static analysis method does not need to run the app. It can efficiently and accurately extract the permissions from an app. Based on this cognition and perspective, in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. The system permissions are extracted in Android application package's (Apk's) manifest file. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of apk (SVOA) of each app. The SVOA and the number of the used permissions are learned and tested by machine learning. 6070 benign apps and 9419 malware are used to evaluate the proposed approach. The experiment results show that only use dangerous permissions or the number of used permissions can't accurately distinguish whether an app is malicious or benign. For malware detection, the proposed approach achieve up to 99.5% accuracy and the learning and training time only needs 0.05s. For malware families detection, the accuracy is 99.6%. The results indicate that the method for unknown/new sample's detection accuracy is 92.71%. Compared against other stateof-the-art approaches, the proposed approach is more effective by detecting malware and malware families.

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“…In [112], an Android malware detection method that use the control flow graph's community structure analysis has been introduced. The proposed method adopts three features extracted from community structures to be used in training and testing some machine learning classifiers namely Decision Tree, SVM, NaiveBayes, and BayesNet.…”

Section: Detection Phasementioning

confidence: 99%

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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An Android Malware Detection Approach Using Community Structures of Weighted Function Call Graphs

Cited by 42 publications

References 27 publications

Exterminating Computational Limits of Machine Learning with Merits of Serverless

Exterminating Computational Limits of Machine Learning with Merits of Serverless

A detection method for android application security based on TF-IDF and machine learning

The Android malware detection systems between hope and reality

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