Machine Learning-Based Lightweight Android Malware Detection System with Static Features

2020 ◽  
pp. 345-359
Author(s):  
Kavita Jain ◽  
Mayank Dave
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection

scholarly journals Significant Permission Identification for Machine Learning Based Android Malware Detection

2021 ◽  
Vol 9 (8) ◽  
pp. 2284-2288
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Detection System ◽  
Malware Detection ◽  
System Level ◽  
Support Vector ◽  
Android Malware ◽  
Android Malware Detection ◽  
Malicious Apps ◽  
Baseline Approach

Abstract: The dreadful rate of growth of malicious apps has become a significant issue that sets back the prosperous mobile scheme. A recent report indicates that a brand new malicious app for golem is introduced each ten seconds. To combat this serious malware campaign, we'd like a scalable malware detection approach that may effectively and expeditiously determine malware apps. varied malware detection tools are developed, together with system-level and network-level approaches. However, scaling the detection for an outsized bundle of apps remains a difficult task. during this paper, we tend to introduce SIGPID, a malware detection system supported permission usage analysis to address the speedy increase within the range of golem malware. rather than extracting and analyzing all golem permissions, we tend to develop 3-levels of pruning by mining the permission information to spot the foremost important permissions that may be effective in identifying between benign and malicious apps. SIGPID then utilizes machine-learning based mostly classification ways to classify totally different families of malware and benign apps. Our analysis finds that solely twenty two permissions square measure important. we tend to then compare the performance of our approach, victimisation solely twenty two permissions, against a baseline approach that analyzes all permissions. The results indicate that once Support Vector Machine (SVM) is employed because the classifier, we are able to bring home the bacon over ninetieth of preciseness, recall, accuracy, and F-measure, that square measure concerning constant as those created by the baseline approach whereas acquisition the analysis times that square measure four to thirty two times but those of victimisation all permissions. Compared against alternative progressive approaches, SIGPID is more practical by sleuthing ninety three.62% of malware within the information set, and 91.4% unknown/new malware samples. Keywords: SIGPID (Significant Permission Identification), SVM(Support Vector Machine), Android, Malware, Benign, Data pruning

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.

An Android Malware Detection System Based on Feature Fusion

2018 ◽  
Vol 27 (6) ◽  
pp. 1206-1213 ◽  
Author(s):  
Jian Li ◽  
Zheng Wang ◽  
Tao Wang ◽  
Jinghao Tang ◽  
Yuguang Yang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Detection System ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection

Towards Deep Learning-Based Approach for Detecting Android Malware

2021 ◽  
pp. 2193-2219
Author(s):  
Jarrett Booz ◽  
Josh McGiff ◽  
William G. Hatcher ◽  
Wei Yu ◽  
James Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Environment ◽  
Malware Detection ◽  
Extensive Study ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Mobile Malware Detection ◽  
Optimal Settings

In this article, the authors implement a deep learning environment and fine-tune parameters to determine the optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, the authors demonstrate the potential performance of a deep learning environment for Android malware detection. Specifically, an extensive study is conducted on various hyper-parameters to determine optimal configurations, and then a performance evaluation is carried out on those configurations to compare and maximize detection accuracy in our target networks. The results achieve a detection accuracy of approximately 95%, with an approximate F1 score of 93%. In addition, the evaluation is extended to include other machine learning frameworks, specifically comparing Microsoft Cognitive Toolkit (CNTK) and Theano with TensorFlow. The future needs are discussed in the realm of machine learning for mobile malware detection, including adversarial training, scalability, and the evaluation of additional data and features.

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