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Android Malware Family Classification Based on Resource Consumption over Time [article]

Luca Massarelli, Leonardo Aniello, Claudio Ciccotelli, Leonardo Querzoni, Daniele Ucci, Roberto Baldoni
2017 arXiv   pre-print
To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScribe.  ...  An important task of malware analysis is the classification of malware samples into known families.  ...  At the time of writing, the most important work on Android malware family classification, based on dynamic analysis, is DroidScribe [5] .  ... 
arXiv:1709.00875v1 fatcat:2rnuz7n7ofdsxdrzesrsmqjaki

AndroDFA: Android Malware Classification Based on Resource Consumption

Luca Massarelli, Leonardo Aniello, Claudio Ciccotelli, Leonardo Querzoni, Daniele Ucci, Roberto Baldoni
2020 Information  
In this paper, we propose AndroDFA (DFA, detrended fluctuation analysis): an approach to Android malware family classification based on dynamic analysis of resource consumption metrics available from the  ...  The vast majority of today's mobile malware targets Android devices. An important task of malware analysis is the classification of malicious samples into known families.  ...  At the time of writing, the most important work on Android malware family classification, based on dynamic analysis, is DroidScribe [14] .  ... 
doi:10.3390/info11060326 fatcat:7tq7isxktzeyvcqd5do7iowapu

InstDroid: A Light Weight Instant Malware Detector for Android Operating Systems

Saba Arshad, Rabia Chaudhary, Munam Ali, Neshmia Hafeez, Muhammad Kamran
2017 International Journal of Advanced Computer Science and Applications  
are still resource inefficient and takes longer time to detect the malicious behavior of applications.  ...  Through experiments, it is shown that InstDroid is an instant malware detector that provides instant security at low resource consumption, power and memory, in comparison to other well-known commercial  ...  Second technology creates profile, based on time in which user uses the Android device.  ... 
doi:10.14569/ijacsa.2017.080822 fatcat:tlshfm27urd2ba25c5o2nxqjx4

MobiSentry: Towards Easy and Effective Detection of Android Malware on Smartphones

Bingfei Ren, Chuanchang Liu, Bo Cheng, Jie Guo, Junliang Chen
2018 Mobile Information Systems  
21,306 malware samples, and (2) malware categorization on DREBIN, the largest labeled Android malware datasets.  ...  In this paper, we present MobiSentry, a novel lightweight defense system for malware classification and categorization on smartphones.  ...  Acknowledgments is work is based on our preliminary version of this study [18] and was supported in part by the National Natural Science  ... 
doi:10.1155/2018/4317501 fatcat:rei5ahohlfaejpa4vqe7mlapsq

Screening smartphone applications using malware family signatures

Jehyun Lee, Suyeon Lee, Heejo Lee
2015 Computers & security  
A set of variants stem from one malware can be considered as one malware family, and malware families cover more than half of the Android malware population.  ...  Available online xxx Keywords: Smartphone security Android Malware Variant detection Static analysis Family signature a b s t r a c t The sharp increase in smartphone malware has become one of the most  ...  We implemented our Android malware detection and family classification mechanism in the C# language and performed time consumption analysis on a desktop PC using our real-world malware samples.  ... 
doi:10.1016/j.cose.2015.02.003 fatcat:u65qngvo4veb7f4mzmp7eupcke

Host-Based Detection and Analysis of Android Malware

Moses Ashawa, Sarah Morris
2019 International Journal for Information Security Research  
The result calls proactive measures rather than proactive in tackling malware infection on Android based mobile devices.  ...  The obtained result shows that some Android families exploit potential privileges on mobile devices.  ...  This paper covers both old and newest Android malware samples and family distributions with a span of over four years.  ... 
doi:10.20533/ijisr.2042.4639.2019.0100 fatcat:7zqrmcrm3fd5bk5zaiser7jgjq

A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms

Zhuo Ma, Haoran Ge, Yang Liu, Meng Zhao, Jianfeng Ma
2019 IEEE Access  
Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets.  ...  Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm.  ...  [12] utilize CNN to build an android malware detection system based on opcode sequence from a disassembled program.  ... 
doi:10.1109/access.2019.2896003 fatcat:53x7yufqvvcnbdcx6dgifzf574

SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

Saba Arshad, Munam A. Shah, Abdul Wahid, Amjad Mehmood, Houbing Song, Hongnian Yu
2018 IEEE Access  
First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods.  ...  For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices.  ...  CONCLUSION This research work is based on the development of a malware detection system that can detect the malwares on the Android devices while ensuring the low resource consumption.  ... 
doi:10.1109/access.2018.2792941 fatcat:vvrkm6rqx5agfelqxtg2s44vqm

Screening Smartphone Applications Using Behavioral Signatures [chapter]

Suyeon Lee, Jehyun Lee, Heejo Lee
2013 IFIP Advances in Information and Communication Technology  
We evaluated our mechanism with 1,759 randomly collected real-world Android applications including 79 variants of 4 malware families.  ...  A counter approach, the behavior analysis to handle the variant issue, takes too much time and resources. We propose a variant detection mechanism using runtime semantic signature.  ...  Our selfdeveloped experimentation program in C++ measures time consumption and detection accuracy on malware variants detection.  ... 
doi:10.1007/978-3-642-39218-4_2 fatcat:7guqwssyrfafhd4gnhge54ulei

DAEMON: Dataset/Platform-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining

Ron Korine, Danny Hendler
2021 IEEE Access  
[24] analyzed malicious Drebin instances and computed classification features based on an application's resource consumption over time. Cai et al.  ...  GoldDream GoldDream is a family of Android Trojans that monitor an infected device and collect sensitive data over time.  ...  Furthermore, by analyzing DAEMON's classification results and selected features, one can gain powerful insights regarding the behavior of different malware families and what differentiates a malicious  ... 
doi:10.1109/access.2021.3082173 fatcat:bccfewzkprghhmxnhkncjblcde

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

Ke Shao, Qiang Xiong, Zhiming Cai, Jesús Díaz-Verdejo
2021 Security and Communication Networks  
Next, we designed two different sampling strategies based on different families of malware to alleviate the sample imbalance in the dataset.  ...  Therefore, this paper proposes a novel malware family-based bagging algorithm for Android malware detection, called FB2Droid, to perform malware detection.  ...  In contrast, static analysis technology is widely used because of its low resource consumption and high code coverage. e problem of Android malware detection is essentially a classification problem.  ... 
doi:10.1155/2021/6642252 fatcat:7j6ulabo2nft5ofhmen4n4g4zq

Android Malware Detection via Graph Representation Learning

Pengbin Feng, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi, Di Lu, Raul Montoliu
2021 Mobile Information Systems  
This paper explores the direction of Android malware detection based on graph representation learning.  ...  Without complex feature graph construction, we propose a new Android malware detection approach based on lightweight static analysis via the graph neural network (GNN).  ...  DroidScribe [41] proposes a multiclass malware family classification method based on dynamic analysis.  ... 
doi:10.1155/2021/5538841 fatcat:o4beznwd4zadvcqfqubbwgalmy

Android Malware Detection Using Parallel Machine Learning Classifiers

Suleiman Y. Yerima, Sakir Sezer, Igor Muttik
2014 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies  
This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware.  ...  Mobile malware has continued to grow at an alarming rate despite on-going efforts towards mitigating the problem.  ...  ACKNOWLEDGMENT We gratefully acknowledge McAfee's support in providing the repository of malware and benign apps used for this research work.  ... 
doi:10.1109/ngmast.2014.23 dblp:conf/ngmast/YerimaSM14 fatcat:sktqormrs5fxndfmzxmoo2iyoa

Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions

Minki Kim, Daehan Kim, Changha Hwang, Seongje Cho, Sangchul Han, Minkyu Park
2021 Applied Sciences  
In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions.  ...  Malware family classification is grouping malware samples that have the same or similar characteristics into the same family.  ...  This article presents a machine-learning-based Android malware family classification.  ... 
doi:10.3390/app112110244 fatcat:tagujv3cifawdi5fpk7eizbvaa

FAMD: a fast multifeature Android malware detection framework, design and implementation

Hongpeng Bai, Nannan Xie, Xiaoqiang Di, Qing Ye
2020 IEEE Access  
malware detection and family classification on the processed features.  ...  We use the FCBF algorithm to reduce the dimension of the features from 2467 to 500. • CatBoost is adopted as the classifier for the first time in Android malware detection and family classification.  ... 
doi:10.1109/access.2020.3033026 fatcat:mtj7j5mekngoxoqnivmxtr3qhm
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