EfficientNet convolutional neural networks-based Android malware detection

Yadav, Pooja; Menon, Neeraj; Ravi, Vinayakumar; Vishvanathan, Sowmya; Pham, Tuan D.

doi:10.1016/j.cose.2022.102622

Cited by 100 publications

(25 citation statements)

References 19 publications

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“…The values we obtained in the second data set are more successful than the work of Yen & Sun (2019) . With the CNN (Efficient B4)-based study by Yadav et al (2022) close values were obtained. The architecture used by Yadav et al (2022) contains 5,330,571 parameters.…”

Section: Resultsmentioning

confidence: 79%

“…With the CNN (Efficient B4)-based study by Yadav et al (2022) close values were obtained. The architecture used by Yadav et al (2022) contains 5,330,571 parameters. In our study, there are 687 parameters in the convolution layers and 243 rules in the ANFIS architecture.…”

Section: Resultsmentioning

confidence: 79%

“…Yadav et al trained and tested 5,986 images with the EfficientNet-B4 CNN architecture, containing the image representations of the dex extension files of android applications. As a result of the test, 95.7% accuracy was obtained ( Yadav et al, 2022 ). Yen and Sun analyzed the APK file of 1,440 good and bad applications and converted these features into images using the word weighting method TF-IDF.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Deep learning-based detection systems can detect malware with high accuracy ( Arslan, 2021 ; Xiao et al, 2019 ). Convolutional neural networks (CNNs), which have been widely used in image analysis in recent years, have also achieved tremendous results on images of malware ( Lachtar, Ibdah & Bacha, 2020 ; Kong et al, 2022 ; Yadav et al, 2022 ). However, despite the success of deep learning architectures, these architectures are disadvantageous due to the need for more resources, high memory consumption and the number of parameters.…”

Section: Introductionmentioning

confidence: 99%

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Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers

Atacak

Kiliç

Doğru

2022

PeerJ Computer Science

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Background Android is the most widely used operating system all over the world. Due to its open nature, the Android operating system has become the target of malicious coders. Ensuring privacy and security is of great importance to Android users. Methods In this study, a hybrid architecture is proposed for the detection of Android malware from the permission information of applications. The proposed architecture combines the feature extraction power of the convolutional neural network (CNN) architecture and the decision making capability of fuzzy logic. Our method extracts features from permission information with a small number of filters and convolutional layers, and also makes the feature size suitable for ANFIS input. In addition, it allows the permission information to affect the classification without being neglected. In the study, malware was obtained from two different sources and two different data sets were created. In the first dataset, Drebin was used for malware applications, and in the second dataset, CICMalDroid 2020 dataset was used for malware applications. For benign applications, the Google Play Store environment was used. Results With the proposed method, 92% accuracy in the first data set and 92% F-score value in the weighted average was achieved. In the second data set, an accuracy of 94.6% and an F-score of 94.6% on the weighted average were achieved. The results obtained in the study show that the proposed method outperforms both classical machine learning algorithms and fuzzy logic-based studies.

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Section: Resultsmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 79%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers

Atacak

Kiliç

Doğru

2022

PeerJ Computer Science

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“…Android applications, written in Java, have received much attention for their malicious detection [33]. Several approaches adopt similar image transformation approaches to perform Android malware detection [34][35][36]. For example, Ding et al [34] proposes a bytecode imagebased malware detection approach.…”

Section: Related Workmentioning

confidence: 99%

BejaGNN: Behavior-based Java Malware Detection via Graph Neural Network

Feng

Yang

et al. 2022

Preprint

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As a popular platform-independent language, Java is widely used in enterprise applications. In the past few years, language vulnerabilities exploited by Java malware have become increasingly prevalent, which cause threats for multi-platform. Security researchers continuously propose various approaches for fighting against Java malware programs. However, the presence of complex hidden techniques, such as code obfuscation, makes identifying complicated Java malware become challenging. Therefore, there is an urgent need to develop new approaches for resisting hidden techniques. In this paper, we present BejaGNN, a novel behavior-based Java malware detection method using static analysis, word embedding technique, and graph neural network. Specifically, BejaGNN leverages static analysis techniques to extract ICFGs from Java program files and then prunes these ICFGs to remove noisy instructions. Then, work embedding techniques are adopted to learn semantic representations for Java bytecode instructions. Finally, BejaGNN builds a graph neural network classifier to determine the maliciousness of Java programs. Experimental results on a public Java bytecode benchmark demonstrate that BejaGNN achieves high F1 98.8% and is superior to existing Java malware detection approaches, which verifies the promise of graph neural network in Java malware detection.

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TrojanDroid: Android Malware Detection for Trojan Discovery Using Convolutional Neural Networks

Seraj,

Pavlidis,

Polatidis

2022

Engineering Applications of Neural Networks

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Android platforms are widely used nowadays in different forms such as mobile phones and tablets, and this has made the Android platform an attractive target for hackers. While there are many solutions available for detecting malware on Android devices there aren't that many that are concentrated on specific malware types. To this extent, this paper delivers a new dataset for Trojan detection for Android apps based on the permissions of the apps, while the second contribution is a neural network architecture that can classify with very high accuracy if an Android app is a genuine app or a Trojan pretending to be a normal app. We have run extensive evaluation tests to validate the performance of the proposed method and we have compared it to other well-known classifiers using well-known evaluation metrics to show its effectiveness.

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EfficientNet convolutional neural networks-based Android malware detection

Cited by 100 publications

References 19 publications

Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers

Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers

BejaGNN: Behavior-based Java Malware Detection via Graph Neural Network

TrojanDroid: Android Malware Detection for Trojan Discovery Using Convolutional Neural Networks

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