scholarly journals Runtime-based Behavior Dynamic Analysis System for Android Malware Detection

2012 ◽  
Author(s):  
Luoxu Min ◽  
Qinghua Cao
Keyword(s):  
Dynamic Analysis ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Analysis System

Runtime-Based Behavior Dynamic Analysis System for Android Malware Detection

2013 ◽  
Vol 756-759 ◽  
pp. 2220-2225 ◽  
Author(s):  
Luo Xu Min ◽  
Qing Hua Cao
Keyword(s):  
Dynamic Analysis ◽  
Private Information ◽  
Malware Detection ◽  
Third Party ◽  
Android Malware ◽  
Android Malware Detection ◽  
Malicious Behavior ◽  
Result Show ◽  
Android Market ◽  
Analysis System

The most serious threats for Android users is come from application, However, the market lack a mechanism to validate whether these applications are malware or not. So, malware maybe leak users private information, malicious deductions for send premium SMS, get root privilege of the Android system and so on. In the traditional method of malware detection, signature is the only basis. It is far enough. In this paper, we propose a runtime-based behavior dynamic analysis for Android malware detection. The new scheme can be implemented as a system. We analyze 350 applications come from third-party Android market, the result show that our system can effectively detect unknown malware and the malicious behavior of malware.

scholarly journals Runtime Detection Framework for Android Malware

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
TaeGuen Kim ◽  
BooJoong Kang ◽  
Eul Gyu Im
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Suffix Tree ◽  
Malware Detection ◽  
Application Programming Interface ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Dynamic Features ◽  
Android Malware ◽  
Android Malware Detection

As the number of Android malware has been increased rapidly over the years, various malware detection methods have been proposed so far. Existing methods can be classified into two categories: static analysis-based methods and dynamic analysis-based methods. Both approaches have some limitations: static analysis-based methods are relatively easy to be avoided through transformation techniques such as junk instruction insertions, code reordering, and so on. However, dynamic analysis-based methods also have some limitations that analysis overheads are relatively high and kernel modification might be required to extract dynamic features. In this paper, we propose a dynamic analysis framework for Android malware detection that overcomes the aforementioned shortcomings. The framework uses a suffix tree that contains API (Application Programming Interface) subtraces and their probabilistic confidence values that are generated using HMMs (Hidden Markov Model) to reduce the malware detection overhead, and we designed the framework with the client-server architecture since the suffix tree is infeasible to be deployed in mobile devices. In addition, an application rewriting technique is used to trace API invocations without any modifications in the Android kernel. In our experiments, we measured the detection accuracy and the computational overheads to evaluate its effectiveness and efficiency of the proposed framework.

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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