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All Journal International Journal of Electrical and Computer Engineering
Narasimhan Ganesh, Enthrakandi
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Computer Science & IT Electrical & Electronics Engineering

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Enhancing 5G network performance through effective resource management with network slicing Suganthi, Nagarajan; Narasimhan Ganesh, Enthrakandi; Guruva Reddy, Elangovan; Balakumar, Vijayaraman; Ilakkiya, Thangam; Varadarajan, Mageshkumar Naarayanasamy; Ramesh Babu, Venkatachalam
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4721-4731

Abstract

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications.
Network intrusion detection system by applying ensemble model for smart home Amru, Malothu; Jagadeesh Kannan, Raju; Narasimhan Ganesh, Enthrakandi; Muthumarilakshmi, Surulivelu; Padmanaban, Kuppan; Jeyapriya, Jeyaprakash; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3485-3494

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 cross-validation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%.
Co-Authors Amru, Malothu Balakumar, Vijayaraman Guruva Reddy, Elangovan Ilakkiya, Thangam Jagadeesh Kannan, Raju Jeyapriya, Jeyaprakash Murugan, Subbiah Muthumarilakshmi, Surulivelu Padmanaban, Kuppan Ramesh Babu, Venkatachalam Suganthi, Nagarajan Varadarajan, Mageshkumar Naarayanasamy