Android Malware Detection Technology Based on Lightweight Convolutional Neural Networks

Ye, Genchao; Zhang, Jian; Li, Huanzhou; Tang, Zhangguo; Lv, Tianzi

doi:https://doi.org/10.1155/2022/8893764

Security and Communication Networks

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Artificial Intelligence Techniques for Securing Smart Devices

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Research Article | Open Access

Volume 2022 | Article ID 8893764 | https://doi.org/10.1155/2022/8893764

Android Malware Detection Technology Based on Lightweight Convolutional Neural Networks

Genchao Ye,^1,2Jian Zhang ,^1,2Huanzhou Li,^1,2Zhangguo Tang,^1,2and Tianzi Lv^1,2

Academic Editor: Usman Habib

Received22 May 2021

Revised09 Jul 2021

Accepted23 Feb 2022

Published16 Mar 2022

Abstract

With the rapid development of Android, a major mobile Internet platform, Android malware attacks have become the number one threat to mobile Internet security. Traditional malware detection methods have low precision and greater time complexity. At present, image detection methods based on deep learning are used in malware detection. However, most of these methods are based on the largescale convolutional neural network model (such as VGG16). The computation and weight files of these models are very large, so they are not suitable for mobile Internet platforms with limited computation. A novel detection method based on a lightweight convolutional neural network is presented in this study. It transforms Android malware classes.dex, Androidmanifest.xml, and resource.arsc into RGB images and uses the lightweight convolutional neural network to extract the features of RGB images automatically. The experimental results of this study indicate that the method performs well in terms of precision and speed of detection.

1. Introduction

Android is one of the most common smartphone operating systems. According to the statistics of the global smartphone market system occupancy in the second quarter of 2018 by Kantar, an international data research organization, 82% of the market share was occupied by Android, and the share is still increasing. The rapid popularization of Android has not only brought great convenience to users but also attracted the attention of network hackers. Due to the openness of Android, Android devices are susceptible to malware infection. AV-TEST Security Report 2019/2020 [1] shows that 6,201,358 new Android malware samples were added in 2017, and the number of new malware samples has increased from 2019. In addition, Android won the first place in 2019 with 417 known security vulnerabilities in the ranking of operating systems and programs with the number of security vulnerabilities and was listed in the common vulnerabilities and exposure (CVE) database. Among all Android malware in 2019, Trojan accounted for more than 90%, followed by malicious adware and ransomware, which are also the main ways for criminals to illegally obtain benefits.

Obviously, it is of extreme urgency to detect and prevent malware in order to resolve serious security problems caused by Android malware. In the past, static analysis technology and dynamic analysis technology [2] were the main methods of Android malware detection. Static analysis [3] refers to the extraction and analysis of the features of the software without running. Lei [4] extracted the API call sequence of the Android software through reverse engineering and normalization and then detected it through a probability discriminant learning model based on regular logistic regression. MaMaDroid [5] constructed the behavior model in the form of Markov chain for the API call sequence and classified malware. Tian [6] solved the problem of repackaged malware detection by analyzing the dependence between classes and classes, methods, and methods according to the heterogeneity of Android malware code. Kong [7] extracted structural information on the malware program as an attribute function call graph and then used an integrated classifier for automatic malware classification. However, it is very difficult to the reverse engineering of the Android software for static analysis, and it is easy to be interfered by code obfuscation technology or encryption technology, resulting in inaccurate detection results. Dynamic analysis technology [8] refers to executing malware in a secure virtual environment and monitoring and extracting malware behavior information dynamically. The MADAM system [9] could judge whether it is malicious according to the seven abnormal behaviors of the software when it is running. Yuan [10] performed a dynamic taint analysis through system hooks and monitored various application operations. Each behavior feature was one-hot encoded and transmitted to the Deep Belief Networks (DBN) as trained data. DroidTrace [11] was a dynamic analysis system with forward execution capability based on the Ptrace which is a dynamic analysis tool. It could judge whether it is malware based on the system dynamic load when the software is running. Somarriba [12] provided a framework of monitoring and visualized abnormal function calls of Android applications. Dynamic analysis technology has a high accuracy rate, but repeated code execution is time-consuming and resource intensive.

In recent years, visualization technology has been introduced into malware detection. Malware visualization technology refers to converting some elements of malware into images’ form for detection. In 2011, Nataraj et al. [13] first proposed to visualize binary files of computer malware as grayscale images and then to classify the malware by using the image features. Mclaughlin et al. [14] extracted the opcode sequence of the code instructions in the Android software and mapped it in the vector space and then used CNN to achieve detection. Kumar et al. [15] transformed malware into grayscale scale images and then detected and classified the malware by Random Forest algorithm (RF). On this basis, Darus et al. [16] compared the accuracy of the K-Nearest Neighbor algorithm (KNN), RF, and Decision Tree algorithm (DT) applied to malware grayscale images’ classification and detection. And they found out the one with the best performance among the three algorithms is RF. Poonguzhali et al. [17] proposed to combine convolutional neural network and intelligent optimization algorithm for malware image detection and achieved high detection accuracy. Many research based on visualization technology has been implemented in computer malware detection, but few on Android malware detection. The detailed analysis of the above references can be found in Table 1.

There are a lot of types and variations of Android malware; their variations and code obfuscation, shell and encryption, make detection more difficult. Traditional convolutional neural network for Android has many parameters and a large amount of calculation, which leads to a long training time and is not suitable for the mobile Internet platform. For the reasons mentioned above, this study proposes to convert Android malware to RGB images and to train and detect it by depthwise separable convolutional neural networks, which improves the training speed and reduces the number of parameters while having fine accuracy.

The rest of this paper is organized as follows. Section 2 describes the basic principle of convolution neural network and depthwise separable convolution. In Section 3, it introduces the method of this study which includes a new malware visualization method and MobileNet V2 network model. Experimental results and performance analysis are discussed in Section 4. And the conclusion of this study is presented in Section 5.

2. Depthwise Separable Convolutional Neural Networks

2.1. Deep Learning and Convolutional Neural Networks

Deep learning [18] is a collection of algorithms that use various machine learning algorithms to solve various problems such as images and texts on multilayer neural networks. It can provide feature learning and obtain higher-level abstract features that human may not recognize. These abstract features can help to capture relevant characteristics. Among deep learning models, convolutional neural networks [19] have outstanding performance in the field of image recognition. Compared with traditional machine learning and other models, CNN has a strong ability to obtain higher-level features of the images. A CNN includes the input layers, the convolution layers, the ReLU excitation layers, the pooling layers, and the fully connected layers. The current common CNN models are VGGNet, ResNet, GoogleNet, and MobileNet.

2.2. Standard Convolution

Standard convolution uses N D_k × D_k convolution kernels to perform convolution calculations on three-channel RGB images and finally output N Feature Maps. The process is shown in Figure 1.

The number of parameters and calculation cost of standard convolution are described as follows:where D_k × D_k represents the size of the convolution kernel, D_k × D_kis the size of the feature maps, and M and N denote the number of input channels and output channels, respectively.

2.3. Depthwise Separable Convolution

Depthwise separable convolution [20]-based MobileNet v2 model is used in this study. Compared with standard convolution, this model has faster calculation speed and lower computational cost. Depthwise separable convolution is a combination of depthwise convolution (DW) and pointwise convolution (PW), in which the number of parameters and calculation cost of extracting feature are lower than standard convolution.(1)Depthwise convolution: after inputting a three-channel RGB image, DW will perform the first convolution operation, which is completely performed in a two-dimensional plane. The number of convolution kernels is the same as the number of channels of the input image. Therefore, a three-channel image generates three feature maps after calculation, as shown in Figure 2.(2)Pointwise convolution: the operation of the PW is similar to the standard convolution operation, as shown in Figure 3. The size of its convolution kernel is , and M is the number of channels in the previous layer. This convolution will weight and combine the previous maps in the depth direction to generate new feature maps.

The number of parameters and calculation cost of the depth separable convolution are represented as follows:

The results of the depthwise convolution and pointwise convolution mentioned above are the same as the result of the standard convolution. Equations (3) and (4) are the ratios of the number of parameters and the calculation cost of the depthwise separable convolution and the standard convolution under the same effect. It can be seen that the number of parameters and the calculation cost of the depthwise separable convolution are greatly reduced. The size of the convolution kernel is set as usually. In this case, the number of parameters and calculation cost of the depthwise separable convolution are about 1/9 of those of the standard convolution:

3. Methods

In this section, this study primarily proposes to visualize Android software as RGB images and then detects it through the lightweight convolution neural network.

3.1. Visualization Process

Android application package (APK) [21] is a file format used by the Android system to install applications (APP), and the suffix is .apk. In fact, the APK can be decompressed by changing the suffix name to .zip, which contains META-INF folder, res folder, resources.arsc, AndroidManifest.xml, and classes.dex. Among them, classes.dex is an 8 bit binary file, which is the logical part of the Android software to realize functions. Resources.arsc is the compiled binary resource file. AndroidManifest.xml contains the configuration information of the APP.

This study proposes to convert Android software to RGB images for detection. RGB [21] images are composed of a three-dimensional array of the format, which can also be understood as being constructed by three grayscale images. The three images represent the three components of R, G, and B. And the pixel point of each component ranges from [0, 255]. From this, the value expression of RGB image pixel points is (R: [0, 255], G: [0, 255], B: [0, 255]). The visualization process is shown in Figure 4. The suffix name of the APK is changed to .zip file. Then, the classes.dex, resources.arsc, and AndroidManifest.xml are extracted by the zip file library of the python. The decimal range of each byte is in [0, 255], which is exactly the same as the value range of each component pixel of the RGB images. So, these three files are filled with channels of R, G, and B to generate RGB images by the PIL library.

3.2. Model Summary

The model named MobileNet v2 [22] implemented in this study is a lightweight CNN model proposed by Google in 2018, which uses depthwise separable convolution. MobileNet v2 is an improvement in MobileNet v1. MobileNet v1 is only a stack of depthwise separable convolutions; however, MobileNet v2 introduces a residual structure to improve the performance of the network. In addition, in order to preserve the diversity of features, the nonlinear activation function of ReLU is not adopted at the end of each depthwise separable convolution. The structure of MobileNet includes DW and PW. Before DW, MobileNet v2 adds a PW for performing dimension upgrade. This method makes DW work in higher dimensions, which can get a better effect. The convolution structure of MobileNet v2 is shown in Figure 5.

Table 2 shows the number of parameters of MobileNet v2 and other models. It can be seen that MobileNet v2 has fewer parameters and faster training speed than MobileNet v1, so this study uses MobileNet v2 for Android malware detection.

3.3. Functions of Training Process

Android malware detection is a two-classification problem. Sigmoid which is a good threshold function is used as the output of the classifier. It can map the output variable to (0, 1). If the output variable is greater than 0.5, it is considered to belong to one category; otherwise, it belongs to another category. The formula for the sigmoid function is as follows:where x represents the score of the sample of this category in the neural network.

Binary_cross entropy is used as the loss function in the training process of CNN, which is described as follows:where is the true label value (0 or 1) and is the predicted value given by the neural network model. At the end of each single training session, the neural networks will calculate the loss and then update the model parameters according to the loss.

4. Experiments and Discussion

This experiment was carried out under Ubuntu 18.04 version, the CPU is 8-core E5-2687W, the memory is 16 GB, the GPU is NVIDIA RTX 2080 TI, and the memory is 11 GB. This study use python and Tensorflow2.0 framework to design CNN models.

4.1. Dataset

The following two datasets were used in this experiment, and the details are shown in Table 3.(1)Dataset (a) contains CIC-AndMal-2017, CIC-AndMal-2020, and 3000 benign Android software collected by our group. This dataset is divided into benign software and malware.(2)Dataset (b) is CIC-AndMal-2020, which contains 5 types of Android software.

The dataset of this experiment has not been found used in other documents before. And this dataset is adopted in this study to compare the performance of the methods based on different visualization methods and different CNN models.

4.2. Comparison of Visualization Methods

In this study, dataset (a) and dataset (b) are separately divided into a training set and a test set of eight to two, and all samples are visualized to RGB images by the method mentioned in Section 3.1. Then, all images are scaled to the same size. Figure 6 shows some 256 × 256 RGB images generated by randomly selected Android benign software and malware. The difference in image texture between benign software and malware is very minor. However, the depthwise separable convolutional neural networks have a strong ability to image recognition and can judge whether it is malware through the subtle difference in the images.

(a)

(b)

The five-fold cross-validation method which can improve the accuracy and stability of the model is used in this experiment. The network parameters are initialized by MobileNet v2. Except for the last 10 layers of training, all the front layers are frozen. In the training process, the initial learning rate is set to 0.00001 and BATCH_SIZE is set to 32. The epoch is set to 30 in the experiment of dataset (a) and 50 for dataset (b).

Traditional visualization methods of Android software include dexToGrayscale and dexToRGBimage. They refer to the conversion of classes. dex files in APK into grayscale images or RGB images. The RGB images which are generated by the classes.dex, resources.arsc, and AndroidManifest.xml can be easily distinguished in convolutional neural networks. The proposed visualization method is compared with the traditional visualization methods in the MobileNet v2 network model. The experiment is shown in Figures 7 and 8 and Table 4.

(a)

(b)

(a)

(b)

According to the results, the dexToGrayscale method needs more epochs to steady state and has the lowest accuracy. The effect of the dexToRGBimage method is better than the dexToGrayscale method. And the proposed method has the highest accuracy in the similar training time.

4.3. Comparison of Models

In this study, the CNN models in the previous document are compared with the MobileNet v2 used in this project, such as VGG16, Inception v3, and MobileNet v1.

The training process for dataset (a) and (b) is shown in Figures 9 and 10. The accuracy and the training time of the four models under the same conditions can be seen from Table 5.

(a)

(b)

(a)

(b)

In the training of dataset (a), the VGG16 model gets the lowest accuracy of 92.12% and the slowest training speed rate of 74.11 seconds at 1 epoch. The MobileNet v1 has fast training spend rate and high accuracy. And the Inception v3 is the opposite of it. The proposed method with MobileNet v2 has the accuracy of 95.98% and the training speed rate of 41.44 seconds at 1 epoch, which gets the highest accuracy in a relatively short time and has a certain improvement compared with the methods in historical document. Training result of dataset (b) is like that of dataset (a), MobileNet v2 takes the accuracy rate of 89.54%, and the training speed rate is 41.44 seconds at 1 epoch.

4.4. Model Analysis

Figure 11 shows the confusion matrix for dataset (a) and (b). In dataset (a), the true positive rate and false negative rate reached 99.3% and 95.2%, respectively. Among them, the recall rate of malicious software is 95.1%. In dataset (b), because of the small number of samples, many kinds and data imbalance, detection results of adware and SMS are not very good, but the true positive rate of banking malware, benign software, and riskware achieved 88.6%, 94.5%, and 82.8%, respectively.

(a)

(b)

Figures 12 and 13 show the P-R curves of dataset (a) and (b). The P-R curve is drawn under certain some thresholds based on precision rate and recall rate, which is an evaluation indicator that shows the quality of the model. In dataset (a), the AUC of malware and benign is 0.978 and 0.982, respectively. In another dataset, AUC of the five categories is 0.839, 0.779, 0.957, 0.906, and 0.968.

Figures 14 and 15 show the ROC curves of dataset (a) and (b). The abscissa of the ROC curve is the false positive rate and the ordinate is true positive rate. And ROC curve is a standard to measure the quality of a classification model. As can be seen from the figures, the model performs better in the margin class and SMS class because the number of other three kinds of data is small. Overall, the MobileNet V2 model used in this study has good performance and stability.

4.5. Discussion

Through the above experiments, it can be seen that the malware visualization method introduced in this study is better than the methods in [17, 23], etc. And its generalization ability is good. The performance and efficiency of MobileNet V2 in this experiment are also superior to those of the conventional convolutional neural network model. However, the model memory consumption of MobileNet V2 is still large. For the forthcoming period, we will consider compressing the network structure by referring to other lightweight network models, such as ShuffleNet [24], or optimizing the feature quantity by combining three feature files to generate a Markov graph [25] to further improve the performance of the detection method.

5. Conclusions

Android malware is continuously causing safety hazards to people's life, so the detection of Android malware is very necessary. This study proposed an Android malware detection method based on depthwise separable convolutional neural networks. The proposed algorithm converts classes.dex, resources.arsc, and AndroidManifest.xml of the Android malware samples to the RGB image. Compared with the grayscale image, RGB image possesses better color and texture characteristics. The quality and accuracy of MobileNet v2 model used in this experiment are higher than other models. Moreover, the number of parameters and calculation cost of MobileNet v2 are far lower than other models. If the model is deployed on Android phones, it can achieve a relatively outstanding malware detection function of the case of occupying a small amount of memory. So, this model is more suitable for Android malware detection. The significant improvement of the proposed method has been demonstrated by comparing it with the methods of some historical research.

Data Availability

Restrictions are applied to the availability of these data. Data were obtained from Canadian Institute for Cybersecurity and are available at https://www.unb.ca/cic/datasets/andmal2020.html with the permission of Canadian Institute for Cybersecurity.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Copyright

Copyright © 2022 Genchao Ye et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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