Evaluation of Android Malware Detection Based on System Calls

2016 ◽  
Author(s):  
Marko Dimjašević ◽  
Simone Atzeni ◽  
Ivo Ugrina ◽  
Zvonimir Rakamaric
Keyword(s):  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
System Calls

scholarly journals AdvAndMal: Adversarial Training for Android Malware Detection and Family Classification

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1081
Author(s):  
Chenyue Wang ◽  
Linlin Zhang ◽  
Kai Zhao ◽  
Xuhui Ding ◽  
Xusheng Wang
Keyword(s):  
Malware Detection ◽  
Generative Adversarial Network ◽  
Android Malware ◽  
Adversarial Network ◽  
Android Malware Detection ◽  
System Calls ◽  
Pros And Cons ◽  
Adversarial Training ◽  
Family Classification ◽  
Detection Technologies

In recent years, Android malware has continued to evolve against detection technologies, becoming more concealed and harmful, making it difficult for existing models to resist adversarial sample attacks. At the current stage, the detection result is no longer the only criterion for evaluating the pros and cons of the model with its algorithms, it is also vital to take the model’s defensive ability against adversarial samples into consideration. In this study, we propose a general framework named AdvAndMal, which consists of a two-layer network for adversarial training to generate adversarial samples and improve the effectiveness of the classifiers in Android malware detection and family classification. The adversarial sample generation layer is composed of a conditional generative adversarial network called pix2pix, which can generate malware variants to extend the classifiers’ training set, and the malware classification layer is trained by RGB image visualized from the sequence of system calls. To evaluate the adversarial training effect of the framework, we propose the robustness coefficient, a symmetric interval i = [−1, 1], and conduct controlled experiments on the dataset to measure the robustness of the overall framework for the adversarial training. Experimental results on 12 families with the largest number of samples in the Drebin dataset show that the accuracy of the overall framework is increased from 0.976 to 0.989, and its robustness coefficient is increased from 0.857 to 0.917, which proves the effectiveness of the adversarial training method.

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
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