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Experimental Study with Real-world Data for Android App Security Analysis using Machine Learning

Sankardas Roy, Jordan DeLoach, Yuping Li, Nic Herndon, Doina Caragea, Xinming Ou, Venkatesh Prasad Ranganath, Hongmin Li, Nicolais Guevara
2015 Proceedings of the 31st Annual Computer Security Applications Conference on - ACSAC 2015  
The large (market-scale) dataset (benign and malicious apps) we use in the above experiments represents the real-world Android app security analysis scale.  ...  Although Machine Learning (ML) based approaches have shown promise for Android malware detection, a set of critical challenges remain unaddressed.  ...  Acknowledgment We thank Daniel Arp, Hugo Gascon, and Konrad Rieck for providing us with additional information on the Drebin work (see Section 3.2.2).  ... 
doi:10.1145/2818000.2818038 dblp:conf/acsac/RoyDLHCORLG15 fatcat:gadjthcjsjfyjp2qcr6lbfz4u4

Authentic learning of mobile security with case studies

Minzhe Guo, Prabir Bhattacharya, Kai Qian, Li Yang
2013 2013 IEEE Frontiers in Education Conference (FIE)  
This work-in-progress paper presents an approach to authentic learning of mobile security through real-world-scenario case studies.  ...  Five sets of case studies are being developed to cover the state-of-the-art of mobile security knowledge and practices.  ...  a virtual machine with experimental settings and data) and analyze the malicious behaviors and features.  ... 
doi:10.1109/fie.2013.6685091 dblp:conf/fie/GuoBQY13 fatcat:ycdye3fpevdjrixmb4v2mfqizi

Machine Learning Based Automotive Forensic Analysis for Mobile Applications Using Data Mining

MD. Hussain Khan, G. Pradeepini
2015 TELKOMNIKA Indonesian Journal of Electrical Engineering  
In the proposed system, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF).In this paper, our proposed system works on machine learning to conduct automotive  ...  Security is very important task in Mobiles and mobile apps. To improve the security status of mobiles, existing methodology is using cloud computing and data mining.  ...  Related Work Mobile phone security becomes very important in real world. Some of the security issues in android applications are: Enck et al.  ... 
doi:10.11591/tijee.v16i2.1623 fatcat:x4itrubtrngefe5tcdvjrwzkry

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

Suhaib Jasim Hamdi, Naaman Omar, Adel AL-zebari, Karwan Jameel Merceedi, Abdulraheem Jamil Ahmed, Nareen O. M. Salim, Sheren Sadiq Hasan, Shakir Fattah Kak, Ibrahim Mahmood Ibrahim, Hajar Maseeh Yasin, Azar Abid Salih
2021 Asian Journal of Research in Computer Science  
Existing research suggests that machine learning is an effective and promising way to detect Android 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.  ...  With machine learning for Android malware detection, it is feasible to identify malware successfully.  ... 
doi:10.9734/ajrcos/2021/v11i330266 fatcat:fcaiwoexh5hk7dce6triq3eu5y

Feature Extraction using Hybrid Analysis for Android Malware Detection Framework

Soe Myint Myat, Myanmar Aerospace Engineering University
2019 International Journal of Engineering Research and  
This paper focus on the extraction of key features from Android apps using hybrid analysis method to improve Machine Learning based detection framework  ...  Today, mobile devices have become a widely used for personal and business purposes.  ...  ACKNOWLEDGMENT The authors are grateful for the supports provided by Myanmar Aerospace Engineering University.  ... 
doi:10.17577/ijertv8is060691 fatcat:2vbkb3isavag3lditlqm3abhua

FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques

Arvind Mahindru, A.L. Sangal
2021 Multimedia tools and applications  
With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malware-infected apps that can steal  ...  and broadly used set of features for malware detection.  ...  In recent study [52] Papilio introduced, a new approach for visualizing permissions of real-world Android apps.  ... 
doi:10.1007/s11042-020-10367-w pmid:33462535 pmcid:PMC7807414 fatcat:rswgmvbizzgwnizpa42sunjmee

A Journey Through Android App Analysis: Solutions and Open Challenges

Jacques Klein
2021 Proceedings of the 2021 International Symposium on Advanced Security on Software and Systems  
In this paper, we will briefly introduce our key contributions in both (1) Android app static analysis to detect security issues, and (2) Android Malware Detection with machine learning.  ...  We will conclude by listing several open challenges that we are currently facing towards improving the analysis and security of Android apps.  ...  These tools have been designed to detect security issues in Android apps. For instance, FlowDroid can be used to detect data leaks in apps.  ... 
doi:10.1145/3457340.3458298 fatcat:ei5vjazjz5akrgj5nlt3cvfodm

Android Malware Detection Using Autoencoder [article]

Abdelmonim Naway, Yuancheng Li
2019 arXiv   pre-print
The experimental results show that the proposed approach can identify malware with high accuracy.  ...  In this paper, we propose a deep learning approach for Android malware detection. The proposed approach investigates five different feature sets and applies Autoencoder to identify malware.  ...  The Mobile Security Framework (MobSF) [16] which, is a tool for Android malware analysis that can perform static analysis and dynamic analysis is used to decompile the apps and generation of smali files  ... 
arXiv:1901.07315v1 fatcat:twvjlibnmfhyldggjmpat6dz5m

Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions

Wei Wang, Meichen Zhao, Zhenzhen Gao, Guangquan Xu, Hequn Xian, Yuanyuan Li, Xiangliang Zhang
2019 IEEE Access  
INDEX TERMS Android system, IoT, security and privacy, machine learning, malware analysis, malapp detection, survey. 67602 2169-3536  ...  Accurately characterizing apps' behaviors, or so-called features, directly affects the detection results with machine learning algorithms. Android apps evolve very fast.  ...  Studies [127] , [160] proposed the suitable system call features used in machine learning for classifying the benign and malicious apps.  ... 
doi:10.1109/access.2019.2918139 fatcat:iifcbw3v4nbb3efq4eutkttcn4

Using Deep Neural Network for Android Malware Detection [article]

Abdelmonim Naway, Yuancheng LI
2019 arXiv   pre-print
To contrive with malicious applications that are increased in volume and sophistication, we propose an Android malware detection system that applies deep learning technique to face the threats of Android  ...  The pervasiveness of the Android operating system, with the availability of applications almost for everything, is readily accessible in the official Google play store or a dozen alternative third-party  ...  Dataset Setup For all the experiments, we look at a dataset of real Android apps and malware.  ... 
arXiv:1904.00736v1 fatcat:hdhohtyvqfdbdoccs45tnxpyea

A Comprehensive Study of Malware Detection in Android Operating Systems

Suhaib Jasim Hamdi, Ibrahim Mahmood Ibrahim, Naaman Omar, Omar M. Ahmed, Zryan Najat Rashid, Awder Mohammed Ahmed, Rowaida Khalil Ibrahim, Shakir Fattah Kak, Hajar Maseeh Yasin, Azar Abid Salih
2021 Asian Journal of Research in Computer Science  
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.  ...  Using machine learning, it is feasible to successfully identify malware with Android malware.  ... 
doi:10.9734/ajrcos/2021/v10i430248 fatcat:wkxtllw4efaexpkrhgg3uwzdne

Deep Learning for Android Malware Defenses: a Systematic Literature Review [article]

Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu
2022 arXiv   pre-print
rules or traditional machine learning may not be effective.  ...  Yet, no systematic literature review focusing on deep learning approaches for Android Malware defenses exists.  ...  On the other hand, there is a large scale of Android apps in the real world, with over 3 million Android apps available through the official store, Google Play.  ... 
arXiv:2103.05292v2 fatcat:qruddq4gknfq7jx5wyrk5qu2eu

A detection method for android application security based on TF-IDF and machine learning

Hongli Yuan, Yongchuan Tang, Wenjuan Sun, Li Liu, Qi Jiang
2020 PLoS ONE  
The SVOA and the number of the used permissions are learned and tested by machine learning. 6070 benign apps and 9419 malware are used to evaluate the proposed approach.  ...  Permissions play a vital role in the security of the Android apps. Term Frequency-Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus.  ...  In section 4, a new Android app security detection approach based on the IF-IDF is proposed. In section 5, the proposed Android app security detection method is used for machine learning classify.  ... 
doi:10.1371/journal.pone.0238694 pmid:32915836 fatcat:tka2xncribbola5mtsrv6rxtui

A Review on The Use of Deep Learning in Android Malware Detection [article]

Abdelmonim Naway, Yuancheng LI
2018 arXiv   pre-print
One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection.  ...  In this work, an extensive survey of static analysis, dynamic analysis, and hybrid analysis that utilized deep learning methods are reviewed with an elaborated discussion on their key concepts, contributions  ...  Recently there were issues raised about machine learning and deep learning security as machine learning algorithms have been devised under the presumption that training and test data pursuing the equivalent  ... 
arXiv:1812.10360v1 fatcat:lncdcxsbyrarnk4pyivus5rn7q

Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset

Zakeya Namrud, Sègla Kpodjedo, Chamseddine Talhi, Ahmed Bali, Alvine Boaye Belle
2021 Applied Sciences  
Our approach relies on that dataset to train Deep Learning (DL) and Support Vector Machine (SVM) models for the detection of Android malware.  ...  As the leading mobile phone operating system, Android is an attractive target for malicious applications trying to exploit the system's security vulnerabilities.  ...  The comparison of modelling results reveals that the deep learning technique is particularly well-suited for Android malware detection, with a high level of 96% accuracy when applied to real-world Android  ... 
doi:10.3390/app11167538 fatcat:smsvslcccveypd2mtyozou6pzq
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