Detecting false messages in the smartphone fault reporting system

Sharmiladevi Rajoo, Pritheega Magalingam, Ganthan Narayana Samy, Nurazean Maarop, Norbik Bashah Idris, Bharanidharan Shanmugam, Sundaresan Perumal

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

The emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device’s false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97% of accuracy and showed the highest detection rate using SVM compared to other classifiers. The result shows that the proposed framework is able to aid digital forensic evidence experts in their initial investigation on detecting false report of a mobile reporting service application in the IoT environment.

Original languageEnglish
Title of host publicationEmerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing
EditorsFaisal Saeed, Fathey Mohammed, Nadhmi Gazem
PublisherSpringer Nature
Pages759-768
Number of pages10
ISBN (Print)9783030335816
DOIs
Publication statusPublished - 2020
Event4th International Conference of Reliable Information and Communication Technology, IRICT 2019 - Johor Bahru, Malaysia
Duration: 22 Sep 201923 Sep 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1073
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference4th International Conference of Reliable Information and Communication Technology, IRICT 2019
CountryMalaysia
CityJohor Bahru
Period22/09/1923/09/19

Fingerprint

Smartphones
Classifiers
Engines
Internet of things

Cite this

Rajoo, S., Magalingam, P., Samy, G. N., Maarop, N., Idris, N. B., Shanmugam, B., & Perumal, S. (2020). Detecting false messages in the smartphone fault reporting system. In F. Saeed, F. Mohammed, & N. Gazem (Eds.), Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing (pp. 759-768). (Advances in Intelligent Systems and Computing; Vol. 1073). Springer Nature. https://doi.org/10.1007/978-3-030-33582-3_71
Rajoo, Sharmiladevi ; Magalingam, Pritheega ; Samy, Ganthan Narayana ; Maarop, Nurazean ; Idris, Norbik Bashah ; Shanmugam, Bharanidharan ; Perumal, Sundaresan. / Detecting false messages in the smartphone fault reporting system. Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. editor / Faisal Saeed ; Fathey Mohammed ; Nadhmi Gazem. Springer Nature, 2020. pp. 759-768 (Advances in Intelligent Systems and Computing).
@inproceedings{ee4924b9dbc641fdbaec2f70e877c5c0,
title = "Detecting false messages in the smartphone fault reporting system",
abstract = "The emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device’s false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97{\%} of accuracy and showed the highest detection rate using SVM compared to other classifiers. The result shows that the proposed framework is able to aid digital forensic evidence experts in their initial investigation on detecting false report of a mobile reporting service application in the IoT environment.",
keywords = "Application, Internet of Things, Reporting services, Smart City, Smartphone, Text classifiers",
author = "Sharmiladevi Rajoo and Pritheega Magalingam and Samy, {Ganthan Narayana} and Nurazean Maarop and Idris, {Norbik Bashah} and Bharanidharan Shanmugam and Sundaresan Perumal",
year = "2020",
doi = "10.1007/978-3-030-33582-3_71",
language = "English",
isbn = "9783030335816",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Nature",
pages = "759--768",
editor = "Faisal Saeed and Fathey Mohammed and Nadhmi Gazem",
booktitle = "Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing",
address = "United States",

}

Rajoo, S, Magalingam, P, Samy, GN, Maarop, N, Idris, NB, Shanmugam, B & Perumal, S 2020, Detecting false messages in the smartphone fault reporting system. in F Saeed, F Mohammed & N Gazem (eds), Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. Advances in Intelligent Systems and Computing, vol. 1073, Springer Nature, pp. 759-768, 4th International Conference of Reliable Information and Communication Technology, IRICT 2019, Johor Bahru, Malaysia, 22/09/19. https://doi.org/10.1007/978-3-030-33582-3_71

Detecting false messages in the smartphone fault reporting system. / Rajoo, Sharmiladevi; Magalingam, Pritheega; Samy, Ganthan Narayana; Maarop, Nurazean; Idris, Norbik Bashah; Shanmugam, Bharanidharan; Perumal, Sundaresan.

Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. ed. / Faisal Saeed; Fathey Mohammed; Nadhmi Gazem. Springer Nature, 2020. p. 759-768 (Advances in Intelligent Systems and Computing; Vol. 1073).

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

TY - GEN

T1 - Detecting false messages in the smartphone fault reporting system

AU - Rajoo, Sharmiladevi

AU - Magalingam, Pritheega

AU - Samy, Ganthan Narayana

AU - Maarop, Nurazean

AU - Idris, Norbik Bashah

AU - Shanmugam, Bharanidharan

AU - Perumal, Sundaresan

PY - 2020

Y1 - 2020

N2 - The emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device’s false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97% of accuracy and showed the highest detection rate using SVM compared to other classifiers. The result shows that the proposed framework is able to aid digital forensic evidence experts in their initial investigation on detecting false report of a mobile reporting service application in the IoT environment.

AB - The emergence of the Internet of Things (IoT) in Smart City allows mobile application developers to develop reporting services with an aim for local citizens to interact with municipalities regarding city issues in an efficient manner. However, the credibility of the messages sent rise as a great challenge when users intentionally send false reports through the application. In this research, an evidence detection framework is developed and divided into three parts that are a data source, IoT device’s false text classification engine and output. Text-oriented digital evidence from an IoT mobile reporting service is analyzed to identify suitable text classifier and to build this framework. The Agile model that consists of define, design, build and test is used for the development of the false text classification engine. Focus given on text-based data that does not include encrypted messages. Our proposed framework able to achieve 97% of accuracy and showed the highest detection rate using SVM compared to other classifiers. The result shows that the proposed framework is able to aid digital forensic evidence experts in their initial investigation on detecting false report of a mobile reporting service application in the IoT environment.

KW - Application

KW - Internet of Things

KW - Reporting services

KW - Smart City

KW - Smartphone

KW - Text classifiers

UR - http://www.scopus.com/inward/record.url?scp=85077774513&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-33582-3_71

DO - 10.1007/978-3-030-33582-3_71

M3 - Conference Paper published in Proceedings

AN - SCOPUS:85077774513

SN - 9783030335816

T3 - Advances in Intelligent Systems and Computing

SP - 759

EP - 768

BT - Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing

A2 - Saeed, Faisal

A2 - Mohammed, Fathey

A2 - Gazem, Nadhmi

PB - Springer Nature

ER -

Rajoo S, Magalingam P, Samy GN, Maarop N, Idris NB, Shanmugam B et al. Detecting false messages in the smartphone fault reporting system. In Saeed F, Mohammed F, Gazem N, editors, Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. Springer Nature. 2020. p. 759-768. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-33582-3_71