Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network

Fahiba Farhin; Ishrat Sultana; Nahida Islam; M. Shamim Kaiser; Md Sazzadur Rahman; Mufti Mahmud

doi:10.1109/ICIEVicIVPR48672.2020.9306666

Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network

Fahiba Farhin
, Ishrat Sultana
, Nahida Islam
, M. Shamim Kaiser
, Md Sazzadur Rahman
, Mufti Mahmud

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

38 Scopus citations

Abstract

Internet of Things (IoT) is a dynamic and distributed wide network system that can integrate a gigantic number of pervasive sensors (i.e., physical objects), wireless nodes, and ubiquitous computing systems. These sensors can collect tons of raw data, send them to the internet at an unprecedented rate, and convert them to actionable insights using computing systems. These sensing nodes or physical objects are vulnerable and have upraised cybersecurity threats. In this work, we proposed the attack detection model for IoT using Software-defined network (SDN). The SDN controller can analyze the traffic flow, detect the anomaly, and block incoming traffic as well as the source nodes. In the SDN, a Fuzzy neural network (FNN) based attack detection system is considered which can detect attacks such as man-in-the-middle, distributed denial of service, side-channel, and malicious code. The FNN is trained and tested using NSL-KDD datasets. The evaluated performance exhibits that the FNN based attack detection system can detect the mentioned attack with an accuracy of 83%.

Original language	English
Title of host publication	2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728193311
DOIs	https://doi.org/10.1109/ICIEVicIVPR48672.2020.9306666
State	Published - 26 Aug 2020
Externally published	Yes
Event	Joint 9th International Conference on Informatics, Electronics and Vision and 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020 - Kitakyushu, Japan Duration: 26 Aug 2020 → 29 Aug 2020

Publication series

Name	2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020

Conference

Conference	Joint 9th International Conference on Informatics, Electronics and Vision and 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020
Country/Territory	Japan
City	Kitakyushu
Period	26/08/20 → 29/08/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Attack Detection
FNN
IoT
NSL-KDD Dataset
SDN

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Information Systems
Electrical and Electronic Engineering
Instrumentation

Access to Document

10.1109/ICIEVicIVPR48672.2020.9306666

Cite this

Farhin, F., Sultana, I., Islam, N., Kaiser, M. S., Rahman, M. S., & Mahmud, M. (2020). Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network. In 2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020 Article 9306666 (2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEVicIVPR48672.2020.9306666

Farhin, Fahiba ; Sultana, Ishrat ; Islam, Nahida et al. / Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network. 2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020).

@inproceedings{aa3b199b4fc84b3b9fe15cec841f6b59,

title = "Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network",

abstract = "Internet of Things (IoT) is a dynamic and distributed wide network system that can integrate a gigantic number of pervasive sensors (i.e., physical objects), wireless nodes, and ubiquitous computing systems. These sensors can collect tons of raw data, send them to the internet at an unprecedented rate, and convert them to actionable insights using computing systems. These sensing nodes or physical objects are vulnerable and have upraised cybersecurity threats. In this work, we proposed the attack detection model for IoT using Software-defined network (SDN). The SDN controller can analyze the traffic flow, detect the anomaly, and block incoming traffic as well as the source nodes. In the SDN, a Fuzzy neural network (FNN) based attack detection system is considered which can detect attacks such as man-in-the-middle, distributed denial of service, side-channel, and malicious code. The FNN is trained and tested using NSL-KDD datasets. The evaluated performance exhibits that the FNN based attack detection system can detect the mentioned attack with an accuracy of 83\%.",

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note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; Joint 9th International Conference on Informatics, Electronics and Vision and 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020 ; Conference date: 26-08-2020 Through 29-08-2020",

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Farhin, F, Sultana, I, Islam, N, Kaiser, MS, Rahman, MS & Mahmud, M 2020, Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network. in 2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020., 9306666, 2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020, Institute of Electrical and Electronics Engineers Inc., Joint 9th International Conference on Informatics, Electronics and Vision and 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020, Kitakyushu, Japan, 26/08/20. https://doi.org/10.1109/ICIEVicIVPR48672.2020.9306666

Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network. / Farhin, Fahiba; Sultana, Ishrat; Islam, Nahida et al.
2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9306666 (2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020).

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

TY - GEN

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AU - Sultana, Ishrat

AU - Islam, Nahida

AU - Kaiser, M. Shamim

AU - Rahman, Md Sazzadur

AU - Mahmud, Mufti

PY - 2020/8/26

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AB - Internet of Things (IoT) is a dynamic and distributed wide network system that can integrate a gigantic number of pervasive sensors (i.e., physical objects), wireless nodes, and ubiquitous computing systems. These sensors can collect tons of raw data, send them to the internet at an unprecedented rate, and convert them to actionable insights using computing systems. These sensing nodes or physical objects are vulnerable and have upraised cybersecurity threats. In this work, we proposed the attack detection model for IoT using Software-defined network (SDN). The SDN controller can analyze the traffic flow, detect the anomaly, and block incoming traffic as well as the source nodes. In the SDN, a Fuzzy neural network (FNN) based attack detection system is considered which can detect attacks such as man-in-the-middle, distributed denial of service, side-channel, and malicious code. The FNN is trained and tested using NSL-KDD datasets. The evaluated performance exhibits that the FNN based attack detection system can detect the mentioned attack with an accuracy of 83%.

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KW - IoT

KW - NSL-KDD Dataset

KW - SDN

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DO - 10.1109/ICIEVicIVPR48672.2020.9306666

M3 - Conference contribution

AN - SCOPUS:85099880010

T3 - 2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020

BT - 2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Farhin F, Sultana I, Islam N, Kaiser MS, Rahman MS, Mahmud M. Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network. In 2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9306666. (2020 Joint 9th International Conference on Informatics, Electronics and Vision and 2020 4th International Conference on Imaging, Vision and Pattern Recognition, ICIEV and icIVPR 2020). doi: 10.1109/ICIEVicIVPR48672.2020.9306666

Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this