SOMDROID: android malware detection by artificial neural network trained using unsupervised learning

2020 ◽  
Author(s):  
Arvind Mahindru ◽  
A. L. Sangal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Unsupervised Learning ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Artificial Neural

scholarly journals BrainShield: A Hybrid Machine Learning-Based Malware Detection Model for Android Devices

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2948
Author(s):  
Corentin Rodrigo ◽  
Samuel Pierre ◽  
Ronald Beaubrun ◽  
Franjieh El Khoury
Keyword(s):  
Neural Network ◽  
Malware Detection ◽  
Detection Methods ◽  
Dynamic Features ◽  
Android Malware ◽  
Detection Model ◽  
The Third ◽  
Android Malware Detection ◽  
Fully Connected ◽  
Server Architecture

Android has become the leading operating system for mobile devices, and the most targeted one by malware. Therefore, many analysis methods have been proposed for detecting Android malware. However, few of them use proper datasets for evaluation. In this paper, we propose BrainShield, a hybrid malware detection model trained on the Omnidroid dataset to reduce attacks on Android devices. The latter is the most diversified dataset in terms of the number of different features, and contains the largest number of samples, 22,000 samples, for model evaluation in the Android malware detection field. BrainShield’s implementation is based on a client/server architecture and consists of three fully connected neural networks: (1) the first is used for static analysis and reaches an accuracy of 92.9% trained on 840 static features; (2) the second is a dynamic neural network that reaches an accuracy of 81.1% trained on 3722 dynamic features; and (3) the third neural network proposed is hybrid, reaching an accuracy of 91.1% trained on 7081 static and dynamic features. Simulation results show that BrainShield is able to improve the accuracy and the precision of well-known malware detection methods.

Apk2Img4AndMal: Android Malware Detection Framework Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Oguz Emre Kural ◽  
Durmus Ozkan Sahin ◽  
Sedat Akleylek ◽  
Erdal Kilic ◽  
Murat Omural
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection

scholarly journals A Hierarchical Approach for Android Malware Detection Using Authorization-Sensitive Features

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 432
Author(s):  
Hui Chen ◽  
Zhengqiang Li ◽  
Qingshan Jiang ◽  
Abdur Rasool ◽  
Lifei Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Malware Detection ◽  
Classification Model ◽  
Gradient Boosting ◽  
Basic Block ◽  
Hierarchical Approach ◽  
Android Malware ◽  
Android Malware Detection ◽  
Extreme Gradient Boosting

Android’s openness has made it a favorite for consumers and developers alike, driving strong app consumption growth. Meanwhile, its popularity also attracts attackers’ attention. Android malware is continually raising issues for the user’s privacy and security. Hence, it is of great practical value to develop a scientific and versatile system for Android malware detection. This paper presents a hierarchical approach to design a malware detection system for Android. It extracts four authorization-sensitive features: basic blocks, permissions, Application Programming Interfaces (APIs), and key functions, and layer-by-layer detects malware based on the similar module and the proposed deep learning model Convolutional Neural Network and eXtreme Gradient Boosting (CNNXGB). This detection approach focuses not only on classification but also on the details of the similarities between malware software. We serialize the key function in light of the sequence of API calls and pick up a similar module that captures the global semantics of malware. We propose a new method to convert the basic block into a multichannel picture and use Convolutional Neural Network (CNN) to learn features. We extract permissions and API calls based on their called frequency and train the classification model by XGBoost. A dynamic similar module feature library is created based on the extracted features to assess the sample’s behavior. The model is trained by utilizing 11,327 Android samples collected from Github, Google Play, Fdroid, and VirusShare. Promising experimental results demonstrate a higher accuracy of the proposed approach and its potential to detect Android malware attacks and reduce Android users’ security risks.

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