A heuristic intrusion detection system for Internet-of-Things (IoT)

Ayyaz-Ul-Haq Qureshi; Hadi Larijani; Jawad Ahmad; Nhamoinesu Mtetwa

doi:10.1007/978-3-030-22871-2_7

A heuristic intrusion detection system for Internet-of-Things (IoT)

Ayyaz-Ul-Haq Qureshi, Hadi Larijani, Jawad Ahmad, Nhamoinesu Mtetwa

Cyber Security and Networks

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

46 Citations (Scopus)

Abstract

Today, digitally connected devices are involved in every aspect of life due to the advancements in Internet-of-Things (IoT) paradigm. Recently, it has been a driving force for a major technological revolution towards the development of advanced modern computer networks connecting physical objects around us. The emergence of IPv6 and installation of open access public networks is attracting cyber-criminals to compromise the user specific security information. This is why the security breaches in IoT devices are dominating the headlines lately. In this research we have developed a random neural network based heuristic intrusion detection system (RNN-IDS) for IoTs. Upon feature selection, the neurons are trained and further tested at different learning rates with NSL-KDD dataset. Two methods are adopted to analyse the proposed scheme where the accuracy of RNN-IDS increased from 85.5% to 95.25%. Results also suggest that upon comparison with other machine learning algorithms, the proposed intelligent intrusion detection has higher accuracy in recognition of anomalous traffic from normal patterns.

Original language	English
Title of host publication	Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing
Editors	Kohei Arai, Rahul Bhatia, Supriya Kapoor
Publisher	Springer Nature
Pages	86-98
Number of pages	13
ISBN (Electronic)	9783030228712
ISBN (Print)	9783030228705
DOIs	https://doi.org/10.1007/978-3-030-22871-2_7
Publication status	Published - 23 Jun 2019
Event	Computing Conference 2019 - London Marriott Hotel Regents Park, London, United Kingdom Duration: 16 Jul 2019 → 17 Jul 2019 https://saiconference.com/Computing

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	997
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Computing Conference 2019
Country/Territory	United Kingdom
City	London
Period	16/07/19 → 17/07/19
Internet address	https://saiconference.com/Computing

Keywords

cyber-Security
intrusion detection systems
IoT security
machine learning
NSL-KDD
random neural networks
Machine learning
Random Neural Networks
Cyber-Security
Intrusion detection systems

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Documents and Links

10.1007/978-3-030-22871-2_7

Cite this

Qureshi, A.-U.-H., Larijani, H., Ahmad, J., & Mtetwa, N. (2019). A heuristic intrusion detection system for Internet-of-Things (IoT). In K. Arai, R. Bhatia, & S. Kapoor (Eds.), Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing (pp. 86-98). (Advances in Intelligent Systems and Computing; Vol. 997). Springer Nature. https://doi.org/10.1007/978-3-030-22871-2_7

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abstract = "Today, digitally connected devices are involved in every aspect of life due to the advancements in Internet-of-Things (IoT) paradigm. Recently, it has been a driving force for a major technological revolution towards the development of advanced modern computer networks connecting physical objects around us. The emergence of IPv6 and installation of open access public networks is attracting cyber-criminals to compromise the user specific security information. This is why the security breaches in IoT devices are dominating the headlines lately. In this research we have developed a random neural network based heuristic intrusion detection system (RNN-IDS) for IoTs. Upon feature selection, the neurons are trained and further tested at different learning rates with NSL-KDD dataset. Two methods are adopted to analyse the proposed scheme where the accuracy of RNN-IDS increased from 85.5% to 95.25%. Results also suggest that upon comparison with other machine learning algorithms, the proposed intelligent intrusion detection has higher accuracy in recognition of anomalous traffic from normal patterns.",

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author = "Ayyaz-Ul-Haq Qureshi and Hadi Larijani and Jawad Ahmad and Nhamoinesu Mtetwa",

note = "AAM and acceptance email req'd x 2 (17/04/19 DC, 11/6/19 ET) Not possible to validate until 1 of these sources. ^Now published, part of book series. ; Computing Conference 2019 ; Conference date: 16-07-2019 Through 17-07-2019",

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Qureshi, A-U-H, Larijani, H, Ahmad, J & Mtetwa, N 2019, A heuristic intrusion detection system for Internet-of-Things (IoT). in K Arai, R Bhatia & S Kapoor (eds), Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing. Advances in Intelligent Systems and Computing, vol. 997, Springer Nature, pp. 86-98, Computing Conference 2019, London, United Kingdom, 16/07/19. https://doi.org/10.1007/978-3-030-22871-2_7

A heuristic intrusion detection system for Internet-of-Things (IoT). / Qureshi, Ayyaz-Ul-Haq; Larijani, Hadi; Ahmad, Jawad et al.
Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing. ed. / Kohei Arai; Rahul Bhatia; Supriya Kapoor. Springer Nature, 2019. p. 86-98 (Advances in Intelligent Systems and Computing; Vol. 997).

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

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AU - Qureshi, Ayyaz-Ul-Haq

AU - Larijani, Hadi

AU - Ahmad, Jawad

AU - Mtetwa, Nhamoinesu

N1 - AAM and acceptance email req'd x 2 (17/04/19 DC, 11/6/19 ET) Not possible to validate until 1 of these sources. ^Now published, part of book series.

PY - 2019/6/23

Y1 - 2019/6/23

N2 - Today, digitally connected devices are involved in every aspect of life due to the advancements in Internet-of-Things (IoT) paradigm. Recently, it has been a driving force for a major technological revolution towards the development of advanced modern computer networks connecting physical objects around us. The emergence of IPv6 and installation of open access public networks is attracting cyber-criminals to compromise the user specific security information. This is why the security breaches in IoT devices are dominating the headlines lately. In this research we have developed a random neural network based heuristic intrusion detection system (RNN-IDS) for IoTs. Upon feature selection, the neurons are trained and further tested at different learning rates with NSL-KDD dataset. Two methods are adopted to analyse the proposed scheme where the accuracy of RNN-IDS increased from 85.5% to 95.25%. Results also suggest that upon comparison with other machine learning algorithms, the proposed intelligent intrusion detection has higher accuracy in recognition of anomalous traffic from normal patterns.

AB - Today, digitally connected devices are involved in every aspect of life due to the advancements in Internet-of-Things (IoT) paradigm. Recently, it has been a driving force for a major technological revolution towards the development of advanced modern computer networks connecting physical objects around us. The emergence of IPv6 and installation of open access public networks is attracting cyber-criminals to compromise the user specific security information. This is why the security breaches in IoT devices are dominating the headlines lately. In this research we have developed a random neural network based heuristic intrusion detection system (RNN-IDS) for IoTs. Upon feature selection, the neurons are trained and further tested at different learning rates with NSL-KDD dataset. Two methods are adopted to analyse the proposed scheme where the accuracy of RNN-IDS increased from 85.5% to 95.25%. Results also suggest that upon comparison with other machine learning algorithms, the proposed intelligent intrusion detection has higher accuracy in recognition of anomalous traffic from normal patterns.

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KW - Cyber-Security

KW - Intrusion detection systems

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DO - 10.1007/978-3-030-22871-2_7

M3 - Conference contribution

SN - 9783030228705

T3 - Advances in Intelligent Systems and Computing

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BT - Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing

A2 - Arai, Kohei

A2 - Bhatia, Rahul

A2 - Kapoor, Supriya

PB - Springer Nature

T2 - Computing Conference 2019

Y2 - 16 July 2019 through 17 July 2019

ER -

A heuristic intrusion detection system for Internet-of-Things (IoT)

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Documents and Links

Fingerprint

Cite this