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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Muthumarilakshmi, Surulivelu
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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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%.
Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms Gurulakshmanan, Gurumoorthi; Amarnath, Raveendra N.; Lebaka, Sivaprasad; Reddy, Munnangi Koti; Mohankumar, Nagarajan; Muthumarilakshmi, Surulivelu; Srinivasan, Chelliah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2427-2435

Abstract

The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.
Co-Authors Aghalya, Stalin Amarnath, Raveendra N. Amru, Malothu Chelliah, Jehan Gurulakshmanan, Gurumoorthi Jagadeesh Kannan, Raju Jesudasan Peter, John Benito Jeyapriya, Jeyaprakash Kannan, Raju Jagadeesh Lebaka, Sivaprasad Mohankumar, Nagarajan Murugan, Subbiah Muthukumaran, Dhakshnamoorthy Narasimhan Ganesh, Enthrakandi Padmanaban, Kuppan Rakesh, Nitin Reddy, Munnangi Koti Rekha, Puttaswamy Sreedevi, Balasubramanian Sreelatha, Tammineni Srinivasan, Chelliah Sujatha, Shanmugam Sujatha, Velusamy Yadav, Karunesh Pratap