scholarly journals An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xin Su ◽  
Qingbo Gong ◽  
Yi Zheng ◽  
Xuchong Liu ◽  
Kuan-Ching Li
Keyword(s):  
Signal Transmission ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Behavioral Characteristics ◽  
Android Application ◽  
Behavioral Characteristic ◽  
Android Malware ◽  
Android Malware Detection ◽  
Android App ◽  
Behavior Characteristics

Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary. The Android platform has developed rapidly because of its good user experience and openness. Meanwhile, these characteristics of this platform, which cause the amazing pace of Android malware, pose a great threat to this platform and data correction during signal transmission of brain-machine interfacing. Many previous works employ various behavioral characteristics to analyze Android application (or app) and detect Android malware to protect signal data secure. However, with the development of Android app, category of Android app tends to be diverse, and the Android malware behavior tends to be complex. This situation makes existing Android malware detections complicated and inefficient. In this paper, we propose a broad analysis, gathering as many behavior characteristics of an app as possible and compare these behavior characteristics in several metrics. First, we extract static and dynamic behavioral characteristic from Android app in an automatic manner. Second, we explain the decision we made in each kind of behavioral characteristic we choose for Android app analysis and Android malware detection. Third, we design a detailed experiment, which compare the efficiency of each kind of behavior characteristic in different aspects. The results of experiment also show Android malware detection performance of these behavior characteristics combine with well-known machine learning algorithms.

scholarly journals Permission Sensitivity-Based Malicious Application Detection for Android

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yubo Song ◽  
Yijin Geng ◽  
Junbo Wang ◽  
Shang Gao ◽  
Wei Shi
Keyword(s):  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Private Data ◽  
Weight Allocation

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

scholarly journals Adversarial Samples on Android Malware Detection Systems for IoT Systems

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 974 ◽  
Author(s):  
Xiaolei Liu ◽  
Xiaojiang Du ◽  
Xiaosong Zhang ◽  
Qingxin Zhu ◽  
Hao Wang ◽  
...  
Keyword(s):  
Detection System ◽  
Fitness Function ◽  
Malware Detection ◽  
Security Analysis ◽  
Machine Learning Algorithms ◽  
Android Malware ◽  
Testing Framework ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Iot Devices

Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.

scholarly journals Heterogeneous Graph Convolutional Networks for Android Malware Detection using Callback-Aware Caller-Callee Graphs

2021 ◽  
Author(s):  
Vinayaka K V ◽  
Jaidhar C D
Keyword(s):  
Malware Detection ◽  
Application Programming Interface ◽  
Extraction Methods ◽  
Android Application ◽  
Convolutional Network ◽  
Android Malware ◽  
Detection Model ◽  
Convolutional Networks ◽  
Android Malware Detection ◽  
Ablation Study

<pre> The popularity of the Android Operating System in the smartphone market has given rise to lots of Android malware. To accurately detect these malware, many of the existing works use machine learning and deep learning-based methods, in which feature extraction methods were used to extract fixed-size feature vectors using the files present inside the Android Application Package (APK). Recently, Graph Convolutional Network (GCN) based methods applied on the Function Call Graph (FCG) extracted from the APK are gaining momentum in Android malware detection, as GCNs are effective at learning tasks on variable-sized graphs such as FCG, and FCG sufficiently captures the structure and behaviour of an APK. However, the FCG lacks information about callback methods as the Android Application Programming Interface (API) is event-driven. This paper proposes enhancing the FCG to eFCG (enhanced-FCG) using the callback information extracted using Android Framework Space Analysis to overcome this limitation. Further, we add permission - API method relationships to the eFCG. The eFCG is reduced using node contraction based on the classes to get R-eFCG (Reduced eFCG) to improve the generalisation ability of the Android malware detection model. The eFCG and R-eFCG are then given as the inputs to the Heterogeneous GCN models to determine whether the APK file from which they are extracted is malicious or not. To test the effectiveness of eFCG and R-eFCG, we conducted an ablation study by removing their various components. To determine the optimal neighbourhood size for GCN, we experimented with a varying number of GCN layers and found that the Android malware detection model using R-eFCG with all its components with four convolution layers achieved maximum accuracy of 96.28%.</pre>

scholarly journals Heterogeneous Graph Convolutional Networks for Android Malware Detection using Callback-Aware Caller-Callee Graphs

2021 ◽  
Author(s):  
Vinayaka K V ◽  
Jaidhar C D
Keyword(s):  
Malware Detection ◽  
Application Programming Interface ◽  
Extraction Methods ◽  
Android Application ◽  
Convolutional Network ◽  
Android Malware ◽  
Detection Model ◽  
Convolutional Networks ◽  
Android Malware Detection ◽  
Ablation Study

<pre> The popularity of the Android Operating System in the smartphone market has given rise to lots of Android malware. To accurately detect these malware, many of the existing works use machine learning and deep learning-based methods, in which feature extraction methods were used to extract fixed-size feature vectors using the files present inside the Android Application Package (APK). Recently, Graph Convolutional Network (GCN) based methods applied on the Function Call Graph (FCG) extracted from the APK are gaining momentum in Android malware detection, as GCNs are effective at learning tasks on variable-sized graphs such as FCG, and FCG sufficiently captures the structure and behaviour of an APK. However, the FCG lacks information about callback methods as the Android Application Programming Interface (API) is event-driven. This paper proposes enhancing the FCG to eFCG (enhanced-FCG) using the callback information extracted using Android Framework Space Analysis to overcome this limitation. Further, we add permission - API method relationships to the eFCG. The eFCG is reduced using node contraction based on the classes to get R-eFCG (Reduced eFCG) to improve the generalisation ability of the Android malware detection model. The eFCG and R-eFCG are then given as the inputs to the Heterogeneous GCN models to determine whether the APK file from which they are extracted is malicious or not. To test the effectiveness of eFCG and R-eFCG, we conducted an ablation study by removing their various components. To determine the optimal neighbourhood size for GCN, we experimented with a varying number of GCN layers and found that the Android malware detection model using R-eFCG with all its components with four convolution layers achieved maximum accuracy of 96.28%.</pre>

scholarly journals A Hybrid Model for Android Malware Detection

2019 ◽  
Vol 8 (12) ◽  
pp. 2656-2662
Keyword(s):  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Dynamic Parameters ◽  
Android Malware ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Android Malware Detection ◽  
Tree Classifier ◽  
Hybrid Detection

Android malware have risen exponentially over the past few years, posing several serious threats such as system damage, financial loss, and mobile botnets. Various detection techniques have been proposed in the literature for Android malware detection. Some of the techniques analyze static parameters such as permissions, or intents, whereas, others focus on dynamic parameters such as network traffic or system calls. Static techniques are relatively easier to implement, however, stealthy recent malware evade static detection by virtue of update attacks. Dynamic detection can be used to detect such stealthy malware, however, it increases the computation overhead. Hence, both kinds of techniques have their own advantages and disadvantages. In this paper, we have proposed an innovative hybrid detection model that uses both static and dynamic features for malware analysis and detection. We first rank the static and dynamic parameters according to the information gain and then apply machine learning algorithms in the testing phase. The results indicate that hybrid approach is better than both static and dynamic approaches and the proposed model achieves 98.9% detection accuracy with Decision Tree classifier

scholarly journals Android Malware Detection Based on a Hybrid Deep Learning Model

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tianliang Lu ◽  
Yanhui Du ◽  
Li Ouyang ◽  
Qiuyu Chen ◽  
Xirui Wang
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Dynamic Feature ◽  
Android Malware ◽  
Android Malware Detection ◽  
Deep Learning Model

In recent years, the number of malware on the Android platform has been increasing, and with the widespread use of code obfuscation technology, the accuracy of antivirus software and traditional detection algorithms is low. Current state-of-the-art research shows that researchers started applying deep learning methods for malware detection. We proposed an Android malware detection algorithm based on a hybrid deep learning model which combines deep belief network (DBN) and gate recurrent unit (GRU). First of all, analyze the Android malware; in addition to extracting static features, dynamic behavioral features with strong antiobfuscation ability are also extracted. Then, build a hybrid deep learning model for Android malware detection. Because the static features are relatively independent, the DBN is used to process the static features. Because the dynamic features have temporal correlation, the GRU is used to process the dynamic feature sequence. Finally, the training results of DBN and GRU are input into the BP neural network, and the final classification results are output. Experimental results show that, compared with the traditional machine learning algorithms, the Android malware detection model based on hybrid deep learning algorithms has a higher detection accuracy, and it also has a better detection effect on obfuscated malware.

scholarly journals Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2813
Author(s):  
Jaehyeong Lee ◽  
Hyuk Jang ◽  
Sungmin Ha ◽  
Yourim Yoon
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection

Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.

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