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Applying machine learning classifiers to dynamic Android malware detection at scale

Brandon Amos, Hamilton Turner, Jules White
2013 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC)  
We also present our STREAM framework, which was developed to enable rapid large-scale validation of mobile malware machine learning classifiers.  ...  Machine learning classifiers are a current method for detecting malicious applications on smartphone systems.  ...  This work is available under the IWCMC-2013 tag at https://github.com/VT-Magnum-Research/antimalware.  ... 
doi:10.1109/iwcmc.2013.6583806 dblp:conf/iwcmc/AmosTW13 fatcat:nk7j4x6yhvbhfkdpfvjfy2m274

Detection of Android Malware App through Feature Extraction and Classification of Android Image

Mohd Abdul Rahim Khan, Nand Kumar, R C Tripathi
2022 International Journal of Advanced Computer Science and Applications  
This paper proposed Machine Learning(ML)based algorithms to detect Android malware apps through feature extraction and classification of grayscale images.  ...  Traditional techniques such as static, dynamic, and hybrid approach, most of the existing approaches require a high rate of human intervention to detect Android malware.  ...  The Local features are classified using machine learning models (KNN, SVM, RF, and AdaBoost) to detect Android malware.  ... 
doi:10.14569/ijacsa.2022.01305103 fatcat:jpiq5ie4hbh47m5ptqxg3wle74

A Review of Android Malware Detection Approaches based on Machine Learning

Kaijun Liu, Shengwei Xu, Guoai Xu, Miao Zhang, Dawei Sun, Haifeng Liu
2020 IEEE Access  
INDEX TERMS Android security, malware detection, machine learning, feature extraction, classifier evaluation.  ...  Existing research suggests that machine learning is an effective and promising way to detect Android malware.  ...  Machine learning theory is widely applied in the detection of Android malware, whether based on static, dynamic, or hybrid analysis approaches.  ... 
doi:10.1109/access.2020.3006143 fatcat:5rn2qg67ezdixkrefwxmyejhsi

Android Malware Detection through Machine Learning Techniques: A Review

Abikoye Oluwakemi Christiana, Benjamin Aruwa Gyunka, Akande Noah
2020 International Journal of Online and Biomedical Engineering (iJOE)  
This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.</p>  ...  Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares.  ...  aiding Android malware detections via Machine Learning.  ... 
doi:10.3991/ijoe.v16i02.11549 fatcat:hurc3snq6zgzzmrbnhvzafxvxu

Adapting Text Categorization for Manifest based Android Malware Detection

Onder Coban, Selma Ozel
2019 Computer Science  
There are mainly three different approaches to detect malware: i) static, ii) dynamic, and iii) hybrid. Static approach analyzes the suspicious program without executing it.  ...  To reach our goal, we apply text mining techniques like feature extraction by using bag-of-words, n-grams, etc. from manifest content of suspicious programs, then apply text classification methods to detect  ...  Wang et al. proposed a method, namely, TextDroid which combines text mining and machine learning to detect Android malwares [54] .  ... 
doi:10.7494/csci.2019.20.3.3285 fatcat:swc7lm4fzrczxjxzuhwcoegfme

Accurate mobile malware detection and classification in the cloud

Xiaolei Wang, Yuexiang Yang, Yingzhi Zeng
2015 SpringerPlus  
Acknowledgements We would like to thank anonymous reviewers for their comments. This research was supported in part by MobileSand-Box group.  ...  Any opinions, findings, and conclusions anomaly detection engine in this material are those of the authors and do not necessarily reflect the views of the funding agencies.  ...  Detection using machine learning and limitation The difficulty of manually crafting and updating detection patterns for Android malware has motivated the application of machine learning.  ... 
doi:10.1186/s40064-015-1356-1 pmid:26543718 pmcid:PMC4628031 fatcat:sg7yzcdnhjaurcusilbqfemcne

Behavioural Analysis of Android Malware using Machine Learning

Lokesh Vaishanav
2017 International Journal Of Engineering And Computer Science  
In this paper we will present various machine learning solutions to counter android malwares that analyse features from malicious application and use those features to classify and detect unknown malicious  ...  This paper summarizes the evolution of malware detection techniques based on machine learning algorithms focused on the android OS.  ...  (ii) Adoption of well-known machine learning classifiers to provide detection of malicious applications in Android.  ... 
doi:10.18535/ijecs/v6i5.32 fatcat:3gneyfuy2fethm5dw6moltc6ce

Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning

Talal A.A Abdullah, Waleed Ali, Rawad Abdulghafor
2020 International Journal of Advanced Computer Science and Applications  
Therefore, the most recently published research studies have suggested machine learning techniques as an alternative method to detect Android malware due to their ability to learn and use the existing  ...  information to detect the new Android malware apps.  ...  Then, the dynamics features are extracted to train the machine learning classifiers in order to be used in Android malware detection.  ... 
doi:10.14569/ijacsa.2020.0110429 fatcat:q6au2thucjhjfny3el5vwhhdqy

MACHINE LEARNING-BASED FRAMEWORK FOR AUTOMATIC MALWARE DETECTION USING ANDROID TRAFFIC DATA

UZOMA RITA ALO, HENRY FRIDAY NWEKE, SYLVESTER I. ELE
2021 Zenodo  
In this paper, we propose to detect malicious apps in android traffic using four (4) different machine learning algorithms.  ...  Although various machine learning algorithms have been proposed recently for malware detection, it is challenging to detection malicious apps with single classification model.  ...  Moreover, the efficiency of dynamic method depends on its ability to detect malicious behavior at runtime.  ... 
doi:10.5281/zenodo.5353433 fatcat:uiih3bdg6nfmjdzimyd3g4crke

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.  ...  Researchers persistently devise countermeasures strategies to fight back malware.  ...  Yuan at al. developed Droid-sec [8] considered the first attempt to apply deep learning in Android malware detection.  ... 
arXiv:1812.10360v1 fatcat:lncdcxsbyrarnk4pyivus5rn7q

Visual Detection for Android Malware using Deep Learning

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Then we used the deep learning technique convolutional neural network to identify Android application as benign or as malware.  ...  The most serious threats to the current mobile internet are Android Malware.  ...  They apply K-means and KNN algorithms to classify the Android application as benign or malware.  ... 
doi:10.35940/ijitee.a8132.1110120 fatcat:ejoz2kgpnjcohaiwhoyzzaf7zi

EMULATOR vs REAL PHONE

Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer
2017 Proceedings of the 3rd ACM on International Workshop on Security And PrivacyAnalytics - IWSPA '17  
Furthermore, all of the studied machine learning based detection performed better when applied to features extracted from the on-device dynamic analysis.  ...  Hence, in this paper we present an investigation of machine learning based malware detection using dynamic analysis on real devices.  ...  STREAM is also a dynamic analysis framework based on Andromaly which enables rapid large-scale validation of mobile malware machine learning classifiers.  ... 
doi:10.1145/3041008.3041010 dblp:conf/codaspy/AlzaylaeeYS17 fatcat:lqjq3po53fhh3dlm2gzi3dqbnu

StormDroid

Sen Chen, Minhui Xue, Zhushou Tang, Lihua Xu, Haojin Zhu
2016 Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security - ASIA CCS '16  
This paper tackles this challenge by introducing a streaminglized machine learning-based MD framework, StormDroid: (i) The core of StormDroid is based on machine learning, enhanced with a novel combination  ...  Mobile devices are especially vulnerable nowadays to malware attacks, thanks to the current trend of increased app downloads.  ...  [41] first proposed to use permission behavior to detect new Android malware and then applies heuristic filtering for detecting unknown Android malware.  ... 
doi:10.1145/2897845.2897860 dblp:conf/ccs/ChenXTXZ16 fatcat:kopitrgwjvdndana4guf6jfigu

Deep-Droid: Deep Learning for Android Malware Detection

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
In this paper, we propose Deep-Droid as a deep learning framework, for detection Android malware.  ...  Machine learning approaches have been showing promising results in classifying malware where most of the method are shallow learners like Random Forest (RF) in recent years.  ...  Machine learning methods have shown promising results in categorizing Android malware.  ... 
doi:10.35940/ijitee.l7889.1091220 fatcat:n7g4leblsrfg7o5kfddsisqb7a

On Malware Detection on Android Smartphones

Eman Shalabi
2020 International Journal for Research in Applied Science and Engineering Technology  
In this paper, we discuss various mobile malware types and datasets used for mobile malware detection process. We also survey various mobile malware detection techniques.  ...  The large increase in the use of smartphones leads to a large increase in generating mobile malware.  ...  analysis and machine learning-based approach for the detection of Android malware by using system calls and network traffic features.  ... 
doi:10.22214/ijraset.2020.6160 fatcat:dv2k5iojjnb6bc3wzlkdurasom
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