A mobile malware detection method using behavior features in network traffic

Wang, Shanshan; Chen, Zhenxiang; Yan, Qiben; Yang, Bo; Peng, Lizhi; Jia, Zhongtian

doi:10.1016/j.jnca.2018.12.014

Cited by 106 publications

(40 citation statements)

References 11 publications

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“…Wang et al [31] presented an effective malware identification and classification method called TrafficAV by combining machine learning algorithm and traffic analysis. And they added a section of the prototype system in TrafficAV in [32].…”

Section: Relate Workmentioning

confidence: 99%

A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic

et al. 2020

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Because of the characteristic of openness and flexibility, Android has become the most popular mobile platform. However, it has also become the most targeted system by mobile malware. It is necessary for the users to have a fast and reliable detection method. In this paper, we proposed a two-layer methods to detect malware. The first layer is permission, intent and component information based static malware detection model. We combine the static features with fully connected neural network to detect the malware and test its effectiveness through experiment, the detection rate of the first layer is 95.22%. Then we input the result (benign apps) into the second layer. In the second layer we used a new cascading CNN and autoencoder CACNN methods to detect network traffic features of apps. The experimental results show that our methods can effectively identify malicious Android apps. The detection rate of the second layer is 99.3% in binary classification. Moreover, the new two-layer model can also detect malware by its category and malicious family. The detection rates are 98.2% and 72.13% respectively.

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Section: Relate Workmentioning

confidence: 99%

A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic

et al. 2020

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“…Wang et al [35] proposed a method which combines analysis of network traffic with the c4.5 machine learning algorithm which according to the authors is capable of identifying Android malware with high accuracy. During the evaluation process the authors tested their model with 8,312 benign apps and 5,560 malware samples.…”

Section: Anomaly-based Detectionmentioning

confidence: 99%

A Survey on Mobile Malware Detection Techniques

Kouliaridis

Barmpatsalou

Kambourakis

et al. 2020

IEICE Trans. Inf. & Syst.

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Modern mobile devices are equipped with a variety of tools and services, and handle increasing amounts of sensitive information. In the same trend, the number of vulnerabilities exploiting mobile devices are also augmented on a daily basis and, undoubtedly, popular mobile platforms, such as Android and iOS, represent an alluring target for malware writers. While researchers strive to find alternative detection approaches to fight against mobile malware, recent reports exhibit an alarming increase in mobile malware exploiting victims to create revenues, climbing towards a billion-dollar industry. Current approaches to mobile malware analysis and detection cannot always keep up with future malware sophistication [2], [4]. The aim of this work is to provide a structured and comprehensive overview of the latest research on mobile malware detection techniques and pinpoint their benefits and limitations.

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“…Due to the increasing number of malignant application in 2016, working on detection models of malwares has accelerated. The studies based on source code analysis in static analysis type [14][15][16], and Sandbox usage [17], client-server structure [18], behaviour based surveillance [19], API calls [20] monitoring researches in dynamic analysis were made. By using different approaches, it was tried to achieve more robust results and to increase the safety of mobile users.…”

Section: Related Workmentioning

confidence: 99%

K-Nearest Neighbour Classifier Usage for Permission Based Malware Detection in Android

Arslan

Yurttakal²

2020

iij

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ABSTRACT Android application platform is making rapid progress in these days. This development has made it the target of malicious application developers. This situation provides a numerical increase in malware apps, diversity in techniques, and rise of damage. Therefore, it is very critical to detect these software and escalation the security of mobile users. Static and dynamic analysis, behaviour scrutiny, machine learning methods are used to ensure security. In this study, K-nearest Neighbourhood (KNN) classifier, one of the machine learning methods, is used. Thus, it is aimed to detect malignant mobile software successfully and quickly. The tests is conducted with dataset includes 492 malware and 697 benign applications. In the proposed algorithm, neighbour number 5 and distance metric is preferred as Minkowski. 80% of dataset randomly selected is reserved for training and 20% for testing. As a result, while 94.1% accuracy is achieved, precision 91.2%, recall 92.7% recall and f1-measure is 92.4%. The high value obtained in f1-measure shows that the proposed model is successful in detecting both malware and benevolent software. The success of using KNN algorithm in classification of malicious apps in the Android has been demonstrated.

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A mobile malware detection method using behavior features in network traffic

Cited by 106 publications

References 11 publications

A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic

A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic

A Survey on Mobile Malware Detection Techniques

K-Nearest Neighbour Classifier Usage for Permission Based Malware Detection in Android

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