scholarly journals A Comprehensive Study of Malware Detection in Android Operating Systems

2021 ◽  
pp. 30-46
Author(s):  
Suhaib Jasim Hamdi ◽  
Ibrahim Mahmood Ibrahim ◽  
Naaman Omar ◽  
Omar M. Ahmed ◽  
Zryan Najat Rashid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Detailed Comparison ◽  
Detection Methods ◽  
Current Status ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Apps ◽  
Android Malware Detection ◽  
Wide Range

Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This  paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static.

scholarly journals Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Wang ◽  
Dafang Zhang ◽  
Xin Su ◽  
Wenjia Li
Keyword(s):  
Machine Learning ◽  
Information Fusion ◽  
Malware Detection ◽  
Parallel Machine ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Detection Model ◽  
Android Apps ◽  
Android Malware Detection

In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.

scholarly journals A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

2021 ◽  
pp. 44-60
Author(s):  
Suhaib Jasim Hamdi ◽  
Naaman Omar ◽  
Adel AL-zebari ◽  
Karwan Jameel Merceedi ◽  
Abdulraheem Jamil Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Malware Detection ◽  
Personal Digital Assistants ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Apps ◽  
Digital World ◽  
Android Malware Detection ◽  
Android Applications

Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.

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.

scholarly journals A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 185
Author(s):  
Vasileios Kouliaridis ◽  
Georgios Kambourakis
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Mobile Malware ◽  
Comprehensive Survey ◽  
Mobile Malware Detection

Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field.

scholarly journals Android Malware Detection Based on Structural Features of the Function Call Graph

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 186
Author(s):  
Yang Yang ◽  
Xuehui Du ◽  
Zhi Yang ◽  
Xing Liu
Keyword(s):  
Malware Detection ◽  
Structural Features ◽  
Coarse Grained ◽  
Detection Methods ◽  
Convolutional Network ◽  
Android Malware ◽  
Call Graph ◽  
Android Apps ◽  
Android Malware Detection ◽  
Function Call

The openness of Android operating system not only brings convenience to users, but also leads to the attack threat from a large number of malicious applications (apps). Thus malware detection has become the research focus in the field of mobile security. In order to solve the problem of more coarse-grained feature selection and larger feature loss of graph structure existing in the current detection methods, we put forward a method named DGCNDroid for Android malware detection, which is based on the deep graph convolutional network. Our method starts by generating a function call graph for the decompiled Android application. Then the function call subgraph containing the sensitive application programming interface (API) is extracted. Finally, the function call subgraphs with structural features are trained as the input of the deep graph convolutional network. Thus the detection and classification of malicious apps can be realized. Through experimentation on a dataset containing 11,120 Android apps, the method proposed in this paper can achieve detection accuracy of 98.2%, which is higher than other existing detection methods.

scholarly journals Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2813
Author(s):  
Jaehyeong Lee ◽  
Hyuk Jang ◽  
Sungmin Ha ◽  
Yourim Yoon
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection

Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.

scholarly journals Make Evasion Harder: An Intelligent Android Malware Detection System

2018 ◽  
Author(s):  
Shifu Hou ◽  
Yanfang Ye ◽  
Yangqiu Song ◽  
Melih Abdulhayoglu
Keyword(s):  
Detection System ◽  
Malware Detection ◽  
Semantic Relatedness ◽  
Application Programming Interface ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Apps ◽  
Android Malware Detection ◽  
Meta Path ◽  
Application Programming

To combat the evolving Android malware attacks, in this paper, instead of only using Application Programming Interface (API) calls, we further analyze the different relationships between them and create higher-level semantics which require more efforts for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning to make predictions. Promising experimental results based on real sample collections from Comodo Cloud Security Center demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.

scholarly journals Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1606
Author(s):  
Janaka Senanayake ◽  
Harsha Kalutarage ◽  
Mhd Omar Al-Kadri
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Effective Means ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Training Examples ◽  
Mobile Malware Detection

With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements. Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

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