Abstract
| Original language | English |
|---|---|
| Title of host publication | Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing |
| Publisher | Springer |
| Pages | 86-98 |
| ISBN (Electronic) | 9783030228712 |
| DOIs | |
| Publication status | Published - 23 Jun 2019 |
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A heuristic intrusion detection system for Internet-of-Things (IoT). / Qureshi, Ayyaz-Ul-Haq; Ahmad, Jawad; Larijani, Hadi; Mtetwa, Nhamoinesu.
Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing. Springer, 2019. p. 86-98.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 - Ahmad, Jawad
AU - Larijani, Hadi
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.
U2 - 10.1007/978-3-030-22871-2_7
DO - 10.1007/978-3-030-22871-2_7
M3 - Conference contribution
SP - 86
EP - 98
BT - Intelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing
PB - Springer
ER -