scholarly journals Enhancing malware detection in Android application by incorporating broadcast receivers

2021 ◽  
Vol 5 (1) ◽  
pp. 36
Author(s):  
Halil Bisgin ◽  
Fadi Mohsen ◽  
Vincent Nwobodo ◽  
Rachael Havens
Keyword(s):  
Malware Detection ◽  
Android Application

Identification of Cryptographic Vulnerability and Malware Detection in Android

2017 ◽  
Vol 11 (3) ◽  
pp. 15-28 ◽  
Author(s):  
Anjali Kumawat ◽  
Anil Kumar Sharma ◽  
Sunita Kumawat
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Malware Detection ◽  
Android Application ◽  
Malicious Attacks ◽  
Web Page ◽  
Static And Dynamic Analysis ◽  
Android System ◽  
User Friendly ◽  
The Web

Android based Smartphones are nowadays getting more popular. While using Smartphone, user is always concerned about security and malicious attacks, cryptographic vulnerability of the applications. With increase in the number of Android mobiles, Android malwares are also increasing very rapidly. So the authors have proposed the “Identification of cryptographic vulnerability and malware detection in Android” system. They have designed a user friendly android application, through which user and developer can easily test the application whether it is benign or vulnerable. The application will be tested firstly using static analysis and then the dynamic analysis will be carried out. The authors have implemented static and dynamic analysis of android application for vulnerable and malicious app detection. They have also created a web page. User can either use the application or the web page.

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 Learning-Based Detection for Malicious Android Application Using Code Vectorization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lin Liu ◽  
Wang Ren ◽  
Feng Xie ◽  
Shengwei Yi ◽  
Junkai Yi ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Malware Detection ◽  
Virus Detection ◽  
Training Model ◽  
Malicious Code ◽  
Detection Methods ◽  
Android Application ◽  
Fusion Model ◽  
Data Set ◽  
Android Applications

The malicious APK (Android Application Package) makers use some techniques such as code obfuscation and code encryption to avoid existing detection methods, which poses new challenges for accurate virus detection and makes it more and more difficult to detect the malicious code. A report indicates that a new malicious app for Android is created every 10 seconds. To combat this serious malware activity, a scalable malware detection approach is needed, which can effectively and efficiently identify the malware apps. Common static detection methods often rely on Hash matching and analysis of viruses, which cannot quickly detect new malicious Android applications and their variants. In this paper, a malicious Android application detection method is proposed, which is implemented by the deep network fusion model. The hybrid model only needs to use the sample training model to achieve high accuracy in the identification of the malicious applications, which is more suitable for the detection of the new malicious Android applications than the existing methods. This method extracts the static features in the core code of the Android application by decompiling APK files, then performs code vectorization processing, and uses the deep learning network for classification and discrimination. Our experiments with a data set containing 10,170 apps show that the decisions from the hybrid model can increase the malware detection rate significantly on a real device, which verifies the superiority of this method in the detection of malicious codes.

Identification of Cryptographic Vulnerability and Malware Detection in Android

2021 ◽  
pp. 58-74
Author(s):  
Anjali Kumawat ◽  
Anil Kumar Sharma ◽  
Sunita Kumawat
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Malware Detection ◽  
Android Application ◽  
Malicious Attacks ◽  
Web Page ◽  
Static And Dynamic Analysis ◽  
Android System ◽  
User Friendly ◽  
The Web

Android based Smartphones are nowadays getting more popular. While using Smartphone, user is always concerned about security and malicious attacks, cryptographic vulnerability of the applications. With increase in the number of Android mobiles, Android malwares are also increasing very rapidly. So the authors have proposed the “Identification of cryptographic vulnerability and malware detection in Android” system. They have designed a user friendly android application, through which user and developer can easily test the application whether it is benign or vulnerable. The application will be tested firstly using static analysis and then the dynamic analysis will be carried out. The authors have implemented static and dynamic analysis of android application for vulnerable and malicious app detection. They have also created a web page. User can either use the application or the web page.

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.

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