Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
An Android Malware Detection Approach Based on Summation of Multi-order Derivatives LSTM
View through CrossRef
Abstract
With the popularity of the Android operating system on mobile devices, unscrupulous people prefer to attack Android devices, which results in the emergence of Android malware. This paper proposes an Android malware detection approach based on Summation of Multi-order Derivatives LSTM (SoMD-LSTM). In order to learn various runtime features of Android malware, the summation of multi-order derivatives is introduced to LSTM. As a result, SoMD-LSTM can describe the dynamic behavior of Android malware quantitatively by the summation of multi-order derivatives. Experiments show that the proposed Android malware detection approach has a better performance than LSTM and other methods.
Title: An Android Malware Detection Approach Based on Summation of Multi-order Derivatives LSTM
Description:
Abstract
With the popularity of the Android operating system on mobile devices, unscrupulous people prefer to attack Android devices, which results in the emergence of Android malware.
This paper proposes an Android malware detection approach based on Summation of Multi-order Derivatives LSTM (SoMD-LSTM).
In order to learn various runtime features of Android malware, the summation of multi-order derivatives is introduced to LSTM.
As a result, SoMD-LSTM can describe the dynamic behavior of Android malware quantitatively by the summation of multi-order derivatives.
Experiments show that the proposed Android malware detection approach has a better performance than LSTM and other methods.
Related Results
AMalLSTM: ANDROID MALWARE DETECTION USING LSTM
AMalLSTM: ANDROID MALWARE DETECTION USING LSTM
Android smartphone apps are becoming increasingly popular, but their security is a concern. Malware can cause damage to mobile devices and servers. Developing detection technologie...
AndroDex: Android Dex Images of Obfuscated Malware
AndroDex: Android Dex Images of Obfuscated Malware
AbstractWith the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to...
An optimal deep learning-based framework for the detection and classification of android malware
An optimal deep learning-based framework for the detection and classification of android malware
The use of smartphones is increasing rapidly and the malicious intrusions associated with it have become a challenging task that needs to be resolved. A secure and effective techn...
An Analysis of Machine Learning-Based Android Malware Detection Approaches
An Analysis of Machine Learning-Based Android Malware Detection Approaches
Abstract
Despite the fact that Android apps are rapidly expanding throughout the mobile ecosystem, Android malware continues to emerge. Malware operations are on the...
A Critical Analysis on Android Vulnerabilities, Malware, Anti-malware and Anti-malware Bypassing
A Critical Analysis on Android Vulnerabilities, Malware, Anti-malware and Anti-malware Bypassing
<p>Android has become the dominant operating system for portable devices, making it a valuable asset that needs protection. Though Android is very popular; it has several vul...
HYBRID DEEP LEARNING EFFECTIVENESS OF IMAGE-BASED MALWARE DETECTION
HYBRID DEEP LEARNING EFFECTIVENESS OF IMAGE-BASED MALWARE DETECTION
The current high rate of malware variant production each day produces hundreds of thousands of new variants making signature detection methods ineffective. Deep learning patterns s...
Clone detection to prevent software piracy in android play store
Clone detection to prevent software piracy in android play store
Android is currently one of the most popular smartphone operating systems. Many application developers have been enticed by the enormous demand for mobile smartphone devices. The a...
Learning-Based Detection for Malicious Android Application Using Code Vectorization
Learning-Based Detection for Malicious Android Application Using Code Vectorization
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 challen...