User relationship classification of facebook messenger mobile data using WEKA

Amber Umair; Priyadarsi Nanda; Xiangjian He; Kim Kwang Raymond Choo

doi:10.1007/978-3-030-02744-5_25

User relationship classification of facebook messenger mobile data using WEKA

Amber Umair^*
, Priyadarsi Nanda
, Xiangjian He
, Kim Kwang Raymond Choo

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Mobile devices are a wealth of information about its user and their digital and physical activities (e.g. online browsing and physical location). Therefore, in any crime investigation artifacts obtained from a mobile device can be extremely crucial. However, the variety of mobile platforms, applications (apps) and the significant size of data compound existing challenges in forensic investigations. In this paper, we explore the potential of machine learning in mobile forensics, and specifically in the context of Facebook messenger artifact acquisition and analysis. Using Quick and Choo (2017)’s Digital Forensic Intelligence Analysis Cycle (DFIAC) as the guiding framework, we demonstrate how one can acquire Facebook messenger app artifacts from an Android device and an iOS device (the latter is, using existing forensic tools. Based on the acquired evidence, we create 199 data-instances to train WEKA classifiers (i.e. ZeroR, J48 and Random tree) with the aim of classifying the device owner’s contacts and determine their mutual relationship strength.

Original language	English
Title of host publication	Network and System Security - 12th International Conference, NSS 2018 - Proceedings
Editors	Man Ho Au, Xiapu Luo, Siu Ming Yiu, Jin Li, Cong Wang, Aniello Castiglione, Kamil Kluczniak
Publisher	Springer Verlag
Pages	337-348
Number of pages	12
ISBN (Print)	9783030027438
DOIs	https://doi.org/10.1007/978-3-030-02744-5_25
Publication status	Published - 2018
Externally published	Yes
Event	12th International Conference on Network and System Security, NSS 2018 - Hong Kong, China Duration: 27 Aug 2018 → 29 Aug 2018

Publication series

Name	Lecture Notes in Computer Science
Volume	11058 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Network and System Security, NSS 2018
Country/Territory	China
City	Hong Kong
Period	27/08/18 → 29/08/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 16 Peace, Justice and Strong Institutions

Free Keywords

Mobile forensics
Social network information forensics
Weka

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-02744-5_25

Cite this

Umair, A., Nanda, P., He, X., & Choo, K. K. R. (2018). User relationship classification of facebook messenger mobile data using WEKA. In M. H. Au, X. Luo, S. M. Yiu, J. Li, C. Wang, A. Castiglione, & K. Kluczniak (Eds.), Network and System Security - 12th International Conference, NSS 2018 - Proceedings (pp. 337-348). (Lecture Notes in Computer Science; Vol. 11058 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-02744-5_25

Umair, Amber ; Nanda, Priyadarsi ; He, Xiangjian et al. / User relationship classification of facebook messenger mobile data using WEKA. Network and System Security - 12th International Conference, NSS 2018 - Proceedings. editor / Man Ho Au ; Xiapu Luo ; Siu Ming Yiu ; Jin Li ; Cong Wang ; Aniello Castiglione ; Kamil Kluczniak. Springer Verlag, 2018. pp. 337-348 (Lecture Notes in Computer Science).

@inproceedings{2fc9853db23445beb9125ebdada1892b,

title = "User relationship classification of facebook messenger mobile data using WEKA",

abstract = "Mobile devices are a wealth of information about its user and their digital and physical activities (e.g. online browsing and physical location). Therefore, in any crime investigation artifacts obtained from a mobile device can be extremely crucial. However, the variety of mobile platforms, applications (apps) and the significant size of data compound existing challenges in forensic investigations. In this paper, we explore the potential of machine learning in mobile forensics, and specifically in the context of Facebook messenger artifact acquisition and analysis. Using Quick and Choo (2017){\textquoteright}s Digital Forensic Intelligence Analysis Cycle (DFIAC) as the guiding framework, we demonstrate how one can acquire Facebook messenger app artifacts from an Android device and an iOS device (the latter is, using existing forensic tools. Based on the acquired evidence, we create 199 data-instances to train WEKA classifiers (i.e. ZeroR, J48 and Random tree) with the aim of classifying the device owner{\textquoteright}s contacts and determine their mutual relationship strength.",

keywords = "Mobile forensics, Social network information forensics, Weka",

author = "Amber Umair and Priyadarsi Nanda and Xiangjian He and Choo, \{Kim Kwang Raymond\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 12th International Conference on Network and System Security, NSS 2018 ; Conference date: 27-08-2018 Through 29-08-2018",

year = "2018",

doi = "10.1007/978-3-030-02744-5\_25",

language = "English",

isbn = "9783030027438",

series = "Lecture Notes in Computer Science",

publisher = "Springer Verlag",

pages = "337--348",

editor = "Au, \{Man Ho\} and Xiapu Luo and Yiu, \{Siu Ming\} and Jin Li and Cong Wang and Aniello Castiglione and Kamil Kluczniak",

booktitle = "Network and System Security - 12th International Conference, NSS 2018 - Proceedings",

address = "Germany",

}

Umair, A, Nanda, P, He, X & Choo, KKR 2018, User relationship classification of facebook messenger mobile data using WEKA. in MH Au, X Luo, SM Yiu, J Li, C Wang, A Castiglione & K Kluczniak (eds), Network and System Security - 12th International Conference, NSS 2018 - Proceedings. Lecture Notes in Computer Science, vol. 11058 LNCS, Springer Verlag, pp. 337-348, 12th International Conference on Network and System Security, NSS 2018, Hong Kong, China, 27/08/18. https://doi.org/10.1007/978-3-030-02744-5_25

User relationship classification of facebook messenger mobile data using WEKA. / Umair, Amber; Nanda, Priyadarsi; He, Xiangjian et al.
Network and System Security - 12th International Conference, NSS 2018 - Proceedings. ed. / Man Ho Au; Xiapu Luo; Siu Ming Yiu; Jin Li; Cong Wang; Aniello Castiglione; Kamil Kluczniak. Springer Verlag, 2018. p. 337-348 (Lecture Notes in Computer Science; Vol. 11058 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - User relationship classification of facebook messenger mobile data using WEKA

AU - Umair, Amber

AU - Nanda, Priyadarsi

AU - He, Xiangjian

AU - Choo, Kim Kwang Raymond

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Mobile devices are a wealth of information about its user and their digital and physical activities (e.g. online browsing and physical location). Therefore, in any crime investigation artifacts obtained from a mobile device can be extremely crucial. However, the variety of mobile platforms, applications (apps) and the significant size of data compound existing challenges in forensic investigations. In this paper, we explore the potential of machine learning in mobile forensics, and specifically in the context of Facebook messenger artifact acquisition and analysis. Using Quick and Choo (2017)’s Digital Forensic Intelligence Analysis Cycle (DFIAC) as the guiding framework, we demonstrate how one can acquire Facebook messenger app artifacts from an Android device and an iOS device (the latter is, using existing forensic tools. Based on the acquired evidence, we create 199 data-instances to train WEKA classifiers (i.e. ZeroR, J48 and Random tree) with the aim of classifying the device owner’s contacts and determine their mutual relationship strength.

AB - Mobile devices are a wealth of information about its user and their digital and physical activities (e.g. online browsing and physical location). Therefore, in any crime investigation artifacts obtained from a mobile device can be extremely crucial. However, the variety of mobile platforms, applications (apps) and the significant size of data compound existing challenges in forensic investigations. In this paper, we explore the potential of machine learning in mobile forensics, and specifically in the context of Facebook messenger artifact acquisition and analysis. Using Quick and Choo (2017)’s Digital Forensic Intelligence Analysis Cycle (DFIAC) as the guiding framework, we demonstrate how one can acquire Facebook messenger app artifacts from an Android device and an iOS device (the latter is, using existing forensic tools. Based on the acquired evidence, we create 199 data-instances to train WEKA classifiers (i.e. ZeroR, J48 and Random tree) with the aim of classifying the device owner’s contacts and determine their mutual relationship strength.

KW - Mobile forensics

KW - Social network information forensics

KW - Weka

UR - https://www.scopus.com/pages/publications/85059035204

U2 - 10.1007/978-3-030-02744-5_25

DO - 10.1007/978-3-030-02744-5_25

M3 - Conference contribution

AN - SCOPUS:85059035204

SN - 9783030027438

T3 - Lecture Notes in Computer Science

SP - 337

EP - 348

BT - Network and System Security - 12th International Conference, NSS 2018 - Proceedings

A2 - Au, Man Ho

A2 - Luo, Xiapu

A2 - Yiu, Siu Ming

A2 - Li, Jin

A2 - Wang, Cong

A2 - Castiglione, Aniello

A2 - Kluczniak, Kamil

PB - Springer Verlag

T2 - 12th International Conference on Network and System Security, NSS 2018

Y2 - 27 August 2018 through 29 August 2018

ER -

Umair A, Nanda P, He X, Choo KKR. User relationship classification of facebook messenger mobile data using WEKA. In Au MH, Luo X, Yiu SM, Li J, Wang C, Castiglione A, Kluczniak K, editors, Network and System Security - 12th International Conference, NSS 2018 - Proceedings. Springer Verlag. 2018. p. 337-348. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-02744-5_25

User relationship classification of facebook messenger mobile data using WEKA

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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