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Network intrusion detection system by applying ensemble model for smart home.

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Title: Network intrusion detection system by applying ensemble model for smart home.
Authors: Amru, Malothu1 malothuamru@gmail.com, Kannan, Raju Jagadeesh2 jagadeeshkannan.r@vit.ac.in, Ganesh, Enthrakandi Narasimhan3 enganesh50@gmail.com, Muthumarilakshmi, Surulivelu4 smlakshmi74@gmail.com, Padmanaban, Kuppan5 padmanaban.k@yahoo.com, Jeyapriya, Jeyaprakash6 priyacseme@gmail.com, Murugan, Subbiah7 smuresjur@gmail.com
Source: International Journal of Electrical & Computer Engineering (2088-8708). Jun2024, Vol. 14 Issue 3, p3485-3494. 10p.
Subjects: Smart homes, HTTP (Computer network protocol), Boosting algorithms, Smart devices, Domestic architecture, Internet of things
Abstract: The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the crossvalidation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the crossvalidation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%. [ABSTRACT FROM AUTHOR]
ISSN:20888708
DOI:10.11591/ijece.v14i3.pp3485-3494