One-dimensional convolutional neural networks for Android malware detection

2018 ◽  
Author(s):  
Chihiro Hasegawa ◽  
Hitoshi Iyatomi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Malware Detection ◽  
Android Malware ◽  
One Dimensional ◽  
Android Malware Detection

scholarly journals FNN and Auto Encoder Deep Learning-Based Algorithm for Android Cyber Security

2020 ◽  
Vol 8 (5) ◽  
pp. 3292-3296
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Detection System ◽  
Malware Detection ◽  
Cyber Attacks ◽  
Open Architecture ◽  
Android Malware ◽  
Android Malware Detection ◽  
Fully Connected

Android is susceptible to malware attacks due to its open architecture, large user base and access to its code. Mobile or android malware attacks are increasing from last year. These are common threats for every internet-accessible device. From Researchers Point of view 50% increase in cyber-attacks targeting Android Mobile phones since last year. Malware attackers increasingly turning their attention to attacking smartphones with credential-theft, surveillance, and malicious advertising. Security investigation in the android mobile system has relied on analysis for malware or threat detection using binary samples or system calls with behavior profile for malicious applications is generated and then analyzed. The resulting report is then used to detect android application malware or threats using manual features. To dispose of malicious applications in the mobile device, we propose an Android malware detection system using deep learning techniques which gives security for mobile or android. FNN(Fully-connected FeedForward Deep Neural Networks) and AutoEncoder algorithm from deep learning provide Extensive experiments on a real-world dataset that reaches to an accuracy of 95 %. These papers explain Deep learning FNN(Fully-connected FeedForward Deep Neural Networks) and AutoEncoder approach for android malware detection.

scholarly journals Chimera: An Android Malware Detection Method Based on Multimodal Deep Learning and Hybrid Analysis

2020 ◽  
Author(s):  
Angelo Schranko de Oliveira ◽  
Renato José Sassi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Dynamic Analysis ◽  
Detection Method ◽  
Malware Detection ◽  
Analysis Data ◽  
Detection Methods ◽  
Feature Engineering ◽  
Android Malware ◽  
Android Malware Detection

<div>The Android Operating System (OS) everywhere, computers, cars, homes, and, of course, personal and corporate smartphones. A recent survey from the International Data Corporation (IDC) reveals that the Android platform holds 85% of the smartphone market share. Its popularity and open nature make it an attractive target for malware. According to AV-TEST, by November 2020, 2.87M new Android malware instances were identified in the wild. Malware detection is a challenging problem that has been actively explored by both the industry and academia using intelligent methods. On the one hand, traditional machine learning (ML) malware detection methods rely on manual feature engineering that requires expert knowledge. On the other hand, deep learning (DL) malware detection methods perform automatic feature extraction but usually require much more data and processing power. In this work, we propose a new multimodal DL Android malware detection method, Chimera, that combines both manual and automatic feature engineering by using the DL architectures, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Transformer Networks (TN) to perform feature learning from raw data (Dalvik Executable (DEX) grayscale images), static analysis data (Android Intents & Permissions), and dynamic analysis data (system call sequences) respectively. To train and evaluate our model, we implemented the Knowledge Discovery in Databases (KDD) process and used the publicly available Android benchmark dataset Omnidroid, which contains static and dynamic analysis data extracted from 22,000 real malware and goodware samples. By leveraging a hybrid source of information to learn high-level feature representations for both the static and dynamic properties of Android applications, Chimera’s detection Accuracy, Precision, Recall, and ROC AUC outperform classical ML algorithms, state-of-the-art Ensemble, and Voting Ensembles ML methods, as well as unimodal DL methods using CNNs, DNNs, TNs, and Long-Short Term Memory Networks (LSTM). To the best of our knowledge, this is the first work that successfully applies multimodal DL to combine those three different modalities of data using DNNs, CNNs, and TNs to learn a shared representation that can be used in Android malware detection tasks.</div>

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