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Malware Detection in Android files based on Multiple levels of Learning and Diverse Data Sources

Authors:

Anitha RamalingamAuthors Info & Claims

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Pages 553 - 559

https://doi.org/10.1145/2791405.2791417

Published: 10 August 2015 Publication History

Abstract

Smart mobile device usage has expanded at a very high rate all over the world. Mobile devices have experienced a rapid shift from pure telecommunication devices to small ubiquitous computing platforms. They run sophisticated operating systems that need to confront the same risks as desktop computers, with Android as the most targeted platform for malware. The processing power is one of the factors that differentiate PC's and mobile phones. Mobile phones are more compact and therefore limited in memory and depend on a limited battery power for their energy needs. Hence developing apps to run on these devices should take into consideration the above mentioned factors. To improve the speed of detection, a multilevel detection mechanism using diverse data sources is designed for detecting malware balancing between the accuracy of detection and usage of less compute intensive computations. In this work we have analyzed android based malware for analysis and a multilevel detection mechanism is designed using diverse data sources. We have evaluated our work on a collection of Android based malware comprising of different malware families and our results show that the proposed method is faster with good performance

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Cited By

Faruki PBhan RJain VBhatia SEl Madhoun NPamula R(2023)A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection FrameworksInformation10.3390/info1407037414:7(374)Online publication date: 30-Jun-2023
https://doi.org/10.3390/info14070374
Caviglione LChoras MCorona IJanicki AMazurczyk WPawlicki MWasielewska K(2021)Tight Arms Race: Overview of Current Malware Threats and Trends in Their DetectionIEEE Access10.1109/ACCESS.2020.30483199(5371-5396)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3048319
Sen SAysan AClark J(2018)SAFEDroid: Using Structural Features for Detecting Android MalwaresSecurity and Privacy in Communication Networks10.1007/978-3-319-78816-6_18(255-270)Online publication date: 25-Apr-2018
https://doi.org/10.1007/978-3-319-78816-6_18
Show More Cited By

Index Terms

Malware Detection in Android files based on Multiple levels of Learning and Diverse Data Sources
1. Information systems
  1. Information systems applications
2. Security and privacy
  1. Systems security
    1. Operating systems security

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cover image ACM Other conferences

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

August 2015

763 pages

ISBN:9781450333610

DOI:10.1145/2791405

Editor:
Indu Nair
SCMS, Kochi, India
,
General Chairs:
Sushmita Mitra
Indian Statistical Institute, Kolkata, India
,
Ljiljana Trajković
Simon Fraser University, Canada
,
Program Chairs:
Punam Bedi
University of Delhi, India
,
Suzanne McIntosh
New York University and Cloudera Inc., USA
,
M. S. Rajasree
IIITM-K, Trivandrum, India

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

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WCI '15

WCI '15: Third International Symposium on Women in Computing and Informatics

August 10 - 13, 2015

Kochi, India

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WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;

Overall Acceptance Rate 98 of 452 submissions, 22%

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Cited By

Faruki PBhan RJain VBhatia SEl Madhoun NPamula R(2023)A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection FrameworksInformation10.3390/info1407037414:7(374)Online publication date: 30-Jun-2023
https://doi.org/10.3390/info14070374
Caviglione LChoras MCorona IJanicki AMazurczyk WPawlicki MWasielewska K(2021)Tight Arms Race: Overview of Current Malware Threats and Trends in Their DetectionIEEE Access10.1109/ACCESS.2020.30483199(5371-5396)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3048319
Sen SAysan AClark J(2018)SAFEDroid: Using Structural Features for Detecting Android MalwaresSecurity and Privacy in Communication Networks10.1007/978-3-319-78816-6_18(255-270)Online publication date: 25-Apr-2018
https://doi.org/10.1007/978-3-319-78816-6_18
Shezan FAfroze SIqbal A(2017)Vulnerability detection in recent Android apps: An empirical study2017 International Conference on Networking, Systems and Security (NSysS)10.1109/NSysS.2017.7885802(55-63)Online publication date: Jan-2017
https://doi.org/10.1109/NSysS.2017.7885802
Karbab EDebbabi MDerhab AMouheb DSchwab SRobertson WBalzarotti D(2016)CypiderProceedings of the 32nd Annual Conference on Computer Security Applications10.1145/2991079.2991124(348-362)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1145/2991079.2991124
Zhang HCole YGe LWei SYu WLu CChen GShen DBlasch EPham K(2016)ScanMe mobileACM SIGAPP Applied Computing Review10.1145/2924715.292471916:1(36-49)Online publication date: 14-Apr-2016
https://dl.acm.org/doi/10.1145/2924715.2924719
DeLoach JCaragea DOu X(2016)Android malware detection with weak ground truth data2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7841008(3457-3464)Online publication date: Dec-2016
https://doi.org/10.1109/BigData.2016.7841008

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