Android malware detection through generative adversarial networks

2019 ◽  
Author(s):  
Muhammad Amin ◽  
Babar Shah ◽  
Aizaz Sharif ◽  
Tamleek Ali ◽  
Ki‐lL Kim ◽  
...  
Keyword(s):  
Malware Detection ◽  
Generative Adversarial Networks ◽  
Android Malware ◽  
Android Malware Detection ◽  
Adversarial Networks

scholarly journals Understanding Deep Learning Architecture to Various Problems of Cyber Security

2021 ◽  
Vol 9 (12) ◽  
pp. 740-746
Author(s):  
Dr. Diwakar Ramanuj Tripathi
Keyword(s):  
Deep Learning ◽  
Cyber Security ◽  
Feature Representation ◽  
Generative Adversarial Networks ◽  
Android Malware ◽  
Security Applications ◽  
Malware Classification ◽  
Android Malware Detection ◽  
Adversarial Networks ◽  
Phishing Detection

Abstract: Traditional machine learning has evolved into deep learning. It's capable of extracting the best feature representation from raw input samples. Intrusion detection, malware classification, Android malware detection, spam and phishing detection, and binary analysis are just a few examples of how this has been used in cyber security. Deep auto encoders, limited Boltzmann machines, recurrent neural networks, generative adversarial networks, and other DL methods are all described in this study in a brief tutorial-style method. After that, we'll go over how each of the DL methods is employed in security applications. Keywords: Machine, Cyber, Security, Architecture, Technology.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

Android malware detection with contrasting permission patterns

2014 ◽  
Vol 11 (8) ◽  
pp. 1-14 ◽  
Author(s):  
Ping Xiong ◽  
Xiaofeng Wang ◽  
Wenjia Niu ◽  
Tianqing Zhu ◽  
Gang Li
Keyword(s):  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection

scholarly journals AFCGDroid: Deep Learning Based Android Malware Detection Using Attributed Function Call Graphs

2020 ◽  
Vol 1693 ◽  
pp. 012080
Author(s):  
Tong Lu ◽  
Xiaoyuan Liu ◽  
Jingwei Chen ◽  
Naitian Hu ◽  
Bo Liu
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Function Call ◽  
Call Graphs
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