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

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
Publisher	Springer
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

Publication series

Name	Advances in Intelligent Systems and Computing book series
Volume	997

Keywords

cyber-Security
intrusion detection systems
IoT security
machine learning
NSL-KDD
random neural networks

Documents and Links

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

Fingerprint Dive into the research topics of 'A heuristic intrusion detection system for Internet-of-Things (IoT)'. Together they form a unique fingerprint.

Cite this

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

@inproceedings{ed4e25936a0b414fa446f3331ae7b614,

title = "A heuristic intrusion detection system for Internet-of-Things (IoT)",

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.",

keywords = "cyber-Security, intrusion detection systems, IoT security, machine learning, NSL-KDD, random neural networks",

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. ",

year = "2019",

month = jun,

day = "23",

doi = "10.1007/978-3-030-22871-2_7",

language = "English",

isbn = "9783030228705",

series = "Advances in Intelligent Systems and Computing book series",

publisher = "Springer",

pages = "86--98",

booktitle = "Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing",

}

A heuristic intrusion detection system for Internet-of-Things (IoT). / Qureshi, Ayyaz-Ul-Haq; Larijani, Hadi; Ahmad, Jawad; Mtetwa, Nhamoinesu.

Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing. Springer, 2019. p. 86-98 (Advances in Intelligent Systems and Computing book series; Vol. 997).

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

TY - GEN

T1 - A heuristic intrusion detection system for Internet-of-Things (IoT)

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.

KW - cyber-Security

KW - intrusion detection systems

KW - IoT security

KW - machine learning

KW - NSL-KDD

KW - random neural networks

U2 - 10.1007/978-3-030-22871-2_7

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

M3 - Conference contribution

SN - 9783030228705

T3 - Advances in Intelligent Systems and Computing book series

SP - 86

EP - 98

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

PB - Springer

ER -