A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks
With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dim...
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| Published in | IEEE transactions on emerging topics in computing Vol. 7; no. 2; pp. 314 - 323 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naïve Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks. |
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| AbstractList | With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naïve Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks. |
| Author | Khayami, Raouf Pajouh, Hamed Haddad Javidan, Reza Choo, Kim-Kwang Raymond Dehghantanha, Ali |
| Author_xml | – sequence: 1 givenname: Hamed Haddad surname: Pajouh fullname: Pajouh, Hamed Haddad email: hp@sutech.ac.ir organization: Department of Computer Engineering and Information Technology, Shiraz University of Technology, Moddares Blvd., Shiraz, Iran – sequence: 2 givenname: Reza surname: Javidan fullname: Javidan, Reza email: reza.javidan@sutech.ac.ir organization: Department of Computer Engineering and Information Technology, Shiraz University of Technology, Moddares Blvd., Shiraz, Iran – sequence: 3 givenname: Raouf surname: Khayami fullname: Khayami, Raouf email: khayami@sutech.ac.ir organization: Department of Computer Engineering and Information Technology, Shiraz University of Technology, Moddares Blvd., Shiraz, Iran – sequence: 4 givenname: Ali surname: Dehghantanha fullname: Dehghantanha, Ali email: A.Dehghantanha@salford.ac.uk organization: University of Salford, Greater Manchester, Salford, United Kingdom – sequence: 5 givenname: Kim-Kwang Raymond orcidid: 0000-0001-9208-5336 surname: Choo fullname: Choo, Kim-Kwang Raymond email: raymond.choo@fulbrightmail.org organization: Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA |
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| Snippet | With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is... |
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| StartPage | 314 |
| SubjectTerms | Anomaly detection CF-KNN Classification Computational modeling Computer networks Datasets Dimensionality reduction Discriminant analysis Feature extraction Hidden Markov models Internet of Things Intrusion detection intrusion detection system Intrusion detection systems IoT Modules multi-layer classification Principal component analysis Reduction Regression analysis |
| Title | A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks |
| URI | https://ieeexplore.ieee.org/document/7762123 https://www.proquest.com/docview/2296108217 |
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