KIRUBA BURI R

@veltech.edu.in

Assistant Professor (Senior Grade), Department of CSE, School of Computing), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology: Chennai, Tamil Nadu, IN
Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 62.

KIRUBA BURI R


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https://researchid.co/kirubaburi
Dr. KirubaBuri R. currently serves as a teaching faculty member in the Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology: Avadi, Chennai, . He has served as a reviewer for many Scopus-indexed journals, and his unwavering dedication to education and innovation underscores his commitment to advancing Holistic knowledge and Spearheading technological growth. His scholarly interests encompass the Internet of Things (IoT), Networking & Security , Adhoc Wireless Networks, Big Data, Cloud Computing, and Machine Learning.

EDUCATION

He completed his Bachelor's degree in Computer Science and Engineering from VRSCET-Arasur, affiliated with Anna University, Chennai, in 2010.
Subsequently, he pursued a Master of Engineering (M.E.) in Pervasive Computing Technology from the UCE BIT Campus, Anna University, Tiruchirappalli, in 2013. In 2017, he earned a Master of Business Administration (MBA) with a specialization in Systems from Manonmaniam Sundaranar University.
He was awarded a Doctor of Philosophy ( in Information and Communication Engineering by Anna University, Chennai, in 2024.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Networks and Communications, Computer Science, Artificial Intelligence
8

Scopus Publications

58

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Adaptive Adversarial-Resilient Explainable Intrusion Detection System for 6G Edge Networks (AARE-IDS)
    Kiruba Buri R., Swaminathan K.
    Reconfigurable Intelligent Surfaces for 6g Enabled Vehicle to Everything Communication, 2026
    The rapid progression towards sixth-generation (6G) edge-intelligent networks requires Intrusion Detection Systems (IDS) that are interpretable, lightweight, and resilient to adversarial maneuvering. Current deep learning-based IDS systems, including CiNeT, present misleading accuracy through many negative factors. Using the CiNeT framework as the foundation of this paper introduces AARE-IDS, Adaptive Adversarially Resilient Explainable Intrusion Detection System (IDS), for real-time 6G edge settings. AARE-IDS system is particularly exceptional amongst similar developments due to its lightweight bijective traffic encoding (NeT2I-Lite), hybrid CNN - Transformer detector, adversarially trained resilience layer, utilizing a federated learning framework to investigate model aggregation and derived from a differential privacy standard for added security while sharing models. Through extensive experiments on the ToN-IoT, UNSW-NB15, and CICIDS-2018 datasets, AARE-IDS achieved 98.9% accuracy, 39% better robustness against FGSM/PGD attacks, and 45% less CPU overhead compared to CiNeT.
  • Optimizing Intrusion Detection Pipelines with AutoML: XGBoost for Enhanced Security
    R. Kiruba Buri, K. Swaminathan, S. Sundarsingh, K. Sankar, P. Yuvarajan
    Lecture Notes in Networks and Systems, 2026
  • Enhancing Security in Heterogeneous IoT Networks through Intelligent Identification Systems
    Journal of VLSI Circuits and Systems, 2025
  • Improving IoT Network Longevity with Attack Repellent Energy (SARE) Algorithm for Energy-Efficient and Secure Routing
    Pandian, Indra, Thirugnasambantham, Shanthi, Balasubramaniam, Karthikeyan, Ravichandran, Kirubaburi
    Tehnicki Vjesnik, 2025
    The current IoT architecture necessitates energy-efficient and secure routing algorithms, particularly in wireless infrastructures where the risk of security issues is elevated.One of the significant challenges is the lack of precise knowledge about the residual energy of nodes, which leads to complications in the Cluster Head (CH) selection process.This research addresses the problem by proposing an attack-repellent algorithm that identifies potential CHs with accurate knowledge of residual energy, while minimizing computational overhead for security purposes.The proposed Secure Attack Repellent Energy (SARE) algorithm selects CHs based on the K-Nearest Neighbor (KNN) algorithm, which evaluates residual energy by considering the battery voltage attached to each node.This algorithm also incorporates key renewal and a secure key exchange mechanism to enhance security, with frequent link key exchanges bolstering the network's robustness against attacks.SARE algorithm introduces a novel method for CH selection that reduces the likelihood of incorrect selections due to imprecise energy information, thereby extending the network's operational lifespan.In addition to energy efficiency, the algorithm emphasizes security by frequently updating encryption keys to guard against potential breaches, ensuring that even if a key is compromised, the damage is limited to a short timeframe.To demonstrate its effectiveness, the SARE algorithm is compared with the Low Energy Adaptive Clustering Hierarchical routing (LEACH) and TSRF algorithms.Results show that the SARE algorithm significantly outperforms these existing protocols.The SARE algorithm extends the network's lifetime by 1.15 times longer than the classical LEACH protocol and improves network throughput by 1.42 times compared to the LEACH routing protocol.Additionally, the SARE algorithm effectively mitigates HOTSPOT and Energy Hole issues, which are common problems in wireless sensor networks.
  • An Intelligent IDS for Mobile Adhoc Networks using Differential Evolutionary and Navie Bayesin Algorithms
    P. Maheswaravenkatesh, K. Nithya, V. Kandasamy, R. Kiruba buri, A. Sumaiya Begum
    Journal of Cybersecurity and Information Management, 2025
  • Trust Aware Water Strider Optimization-Based Clustering with Intrusion Detection in IoT Environments
    Alzaben, Nada, Maashi, Mashael, Alshammeri, Menwa, Saraswathi, V., Buri, R. Kiruba, et al.
    Tehnicki Vjesnik, 2025
    The Internet of Things (IoT) has emerged as a transformative technology revolutionizing domains such as healthcare, smart cities, and e-governance. However, real-time IoT integration faces significant hurdles due to the limited energy resources and computational capabilities of IoT devices. Additionally, security vulnerabilities in IoT systems amplify threats to the integrity and reliability of smart applications, necessitating robust solutions. This study introduces a novel Trust-Aware Improved Water Strider Optimization-based Clustering with Intrusion Detection System (TAIWSOC-IDS) for enhancing IoT environments' security and efficiency. The proposed framework comprises two key stages: clustering and intrusion detection. In the clustering stage, the TAIWSOC technique identifies clusters and cluster heads (CHs) by optimizing a fitness function that considers residual energy, communication distance, and trust metrics, ensuring balanced energy consumption and secure communication. In the intrusion detection stage, a Chaos Game Optimization (CGO)-enhanced Multihead Attention Bidirectional Long Short-Term Memory (MHA-BiLSTM) model is employed. The CGO algorithm optimally tunes the hyper parameters of the MHA-BiLSTM model, improving its ability to accurately classify normal and malicious activities. Experimental evaluation demonstrates that the TAIWSOC-IDS outperforms existing state-of-the-art methods in terms of energy efficiency, clustering reliability, and intrusion detection accuracy. The proposed system achieves enhanced detection rates while significantly reducing false positives, proving its efficacy in mitigating security threats. By addressing both energy efficiency and security concerns, TAIWSOC-IDS serves as a comprehensive framework for developing secure, sustainable, and efficient IoT applications across diverse real-time scenarios.
  • Optimal mixed Kernel extreme learning machine-based intrusion detection system for secure intelligent edge computing
    Reconnoitering the Landscape of Edge Intelligence in Healthcare, 2024
  • Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment
    T. Jayasankar, R. Kiruba Buri, P. Maheswaravenkatesh
    Journal of Forecasting, 2024
    Internet of Things (IoT), cloud computing, and other significant advancements in communication have created new security challenges. Due to these advancements and the ineffectiveness of the current security measures, cyber‐attacks are also increasing quickly. Recently, several artificial intelligence (AI)–based solutions have been presented for various secure applications, such as intrusion detection. This article proposes an intrusion detection system using dynamic search fireworks optimization–based feature selection with optimal deep recurrent neural network (DFWAFS‐ODRNN) model in IoT environment. The presented DFWAFS‐ODRNN model follows a two‐stage process, namely, feature selection and intrusion classification. In the first phase, the DFWAFS‐ODRNN model elects an optimal subset of features using the dynamic search fireworks optimization algorithm (DFWAFS) technique. Next, in the second stage, the intrusions are identified and categorized using the DRNN model. At last, the hyperparameters of the DRNN are optimally chosen by the Nadam optimizer. A detailed simulation analysis of the DFWAFS‐ODRNN model is validated on benchmark intrusion detection system (IDS) dataset, and the outcomes show the efficacy of intrusion detection. The proposed model efficiently detects the intrusion detection with an accuracy of 96.11%.

RECENT SCHOLAR PUBLICATIONS

  • Strengthening of Cyber-Physical Infrastructure Resilience by DRL Adaptive Resource Management
    VVAA K. Swaminathan, R. Kiruba Buri, R. K. Harish
    Engineering Cyber-Physical Systems and Critical Infrastructures 20, 201–213 , 2026
    2026
  • Adaptive Intelligent Routing Algorithm for Integrating MANETs with IoT Networks
    AAGS K. Swaminathan, K. Dhanasekaran, R. Kiruba Buri
    CIS 2025. Lecture Notes in Networks and Systems, 1828, 248–259 , 2026
    2026
  • Optimizing Intrusion Detection Pipelines
    RK Buri, K Swaminathan, S Sundarsingh, K Sankar, P Yuvarajan
    Proceedings of International Conference on Communication and Computational … , 2026
    2026
  • IoT-Driven Predictive Analytics for Smart Agriculture Using Random Forest Algorithm
    RKKS R. Kiruba Buri, K. Swaminathan, S. Sundarsingh
    Proceedings of International Conference on Information Technology and … , 2026
    2026
    Citations: 2
  • Adaptive Adversarial-Resilient Explainable Intrusion Detection System for 6G Edge Networks (AARE-IDS)
    I Kiruba Buri R. (Department of CSE, School of Computing, Vel Tech ...
    Reconfigurable Intelligent Surfaces for 6G-Enabled Vehicle-to-Everything … , 2026
    2026
  • Optimizing Intrusion Detection Pipelines with AutoML: XGBoost for Enhanced Security
    PY R. Kiruba Buri , K. Swaminathan, S. Sundarsingh , K. Sankar
    Proceedings of International Conference on Communication and Computational … , 2026
    2026
  • Support Vector Machine-Lyapunov Control for Nonlinear Cluster Synchronization
    LB Dr.R.Kiruba buri, Dr. K. Swaminathan, Dr.A.Arivazhagi
    Soft Computing: Theories and Applications (SoCTA ) -2025, Soft Computing … , 2025
    2025
  • Block Chain-Reliable Incentive Cooperation System for Multiple Ways of Hopping Wireless Type of Network Infrastructure
    KSKK R. Kiruba Buri, Raunak Kumar )
    Ensuring Secure Connectivity Through AI-Powered Wireless Systems 8, 269-298. , 2025
    2025
  • Protecting Distributed Ledgers from Advanced Persistent Threats Using SVM-Based Blockchain Security
    KS R.Kiruba buri
    International Journal of communication and computer Technologies 13 (2), 11-17 , 2025
    2025
  • Trust Aware Water Strider Optimization-Based Clustering with Intrusion Detection in IoT Environments
    N Alzaben, M Maashi, M Alshammeri, V Saraswathi, RK Buri, SG Priya
    Tehnički vjesnik 32 (6), 2253-2260 , 2025
    2025
  • Hybrid Augmented Edge AI using Lightweight Knowledge Distillation and Incremental Learning with MobileNet for Real-Time Anomaly Detection in Resource-Constrained Smart Environments
    SB R.Kiruba Buri, K. Swaminathan, A.Arivazhagi
    7th International Conference on Communication and Intelligent Systems (ICCIS … , 2025
    2025
  • Adaptive Intelligent Routing Algorithm for Integrating MANETs with IoT Networks
    GS K. Swaminathan, K.Dhanasekaran, R.Kiruba Buri, A.Arivazhagi
    6th Congress on Intelligent Systems (CIS 2025), (IETE), Delhi Centre Soft … , 2025
    2025
  • Enhancing Security in Heterogeneous IoT Networks through Intelligent Identification Systems
    R. Kiruba Buri, Seema Babusing Rathod, K. Swaminathan, Bhavna Bajpai ...
    Journal of VLSI circuits and systems 7 (1), 155-166 , 2025
    2025
    Citations: 6
  • Supply Chain Management Processing Device
    RB R.Kiruba buri, A.Arivazhagi, Swaminathan.K
    IN Patent CBR NO: 208,262 , 2025
    2025
  • NETWORKS AND SECURITY
    DSK Dr. Kiruba Buri R.
    ISBN: 978-93-6260-744-7 Charulatha Publications , 2025
    2025
  • ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
    DSK Dr. Kiruba Buri R.
    ISBN: 978-93-6260-348-7 Charulatha Publications , 2025
    2025
  • Optimizing Intrusion Detection Pipelines with AutoML: XGBoost for Enhanced Security
    YP R .Kiruba Buri, K. Swaminathan, Sundarsingh .S, Sankar .K
    7th International Conference on Communication and Computational Technologies … , 2025
    2025
  • Big Data Analytics
    DKS Dr.R.Kiruba Buri, Dr.B.Rajappa
    PENCIL BITZ , ISBN : 978-93-48556-95-0 , 2025
    2025
  • IoT-Driven Predictive Analytics for Smart Agriculture using Random Forest Algorithm
    DSKMSSMKR Sankar K
    International Conference on Information Technology and Artificial … , 2025
    2025
  • Improving IoT Network Longevity with Attack Repellent Energy (SARE) Algorithm for Energy-Efficient and Secure Routing
    KR Indra PANDIAN*, Shanthi THIRUGNASAMBANTHAM, Karthikeyan BALASUBRAMANIAM
    Technical gazette 32 (3), 876-882 , 2025
    2025
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment
    T Jayasankar, R Kiruba Buri, P Maheswaravenkatesh
    Journal of Forecasting 43 (2), 415-428 , 2024
    2024
    Citations: 26
  • Intelligence Intrusion Detection Using PSO with Decision Tree Algorithm for Adhoc Network
    RK Buri, T Jayasankar
    Bioscience Biotechnology Research Communications 12 (2), 27-34 , 2019
    2019
    Citations: 11
  • Improving IoT Network Longevity with Attack Repellent Energy (SARE) Algorithm for Energy-Efficient and Secure Routing
    KR Indra PANDIAN*, Shanthi THIRUGNASAMBANTHAM, Karthikeyan BALASUBRAMANIAM
    Technical gazette 32 (3), 876-882 , 2025
    2025
    Citations: 8
  • Enhancing Security in Heterogeneous IoT Networks through Intelligent Identification Systems
    R. Kiruba Buri, Seema Babusing Rathod, K. Swaminathan, Bhavna Bajpai ...
    Journal of VLSI circuits and systems 7 (1), 155-166 , 2025
    2025
    Citations: 6
  • Optimal Mixed Kernel Extreme Learning Machine-Based Intrusion Detection System for Secure Intelligent Edge Computing
    RP Selvam, T Jayasankar, RK Buri, P Maheswaravenkatesh
    Reconnoitering the Landscape of Edge Intelligence in Healthcare, 231-248 , 2024
    2024
    Citations: 4
  • IoT-Driven Predictive Analytics for Smart Agriculture Using Random Forest Algorithm
    RKKS R. Kiruba Buri, K. Swaminathan, S. Sundarsingh
    Proceedings of International Conference on Information Technology and … , 2026
    2026
    Citations: 2
  • An Intelligent IDS for Mobile Adhoc Networks using Differential Evolutionary and Navie Bayesin Algorithms.
    P Maheswaravenkatesh, K Nithya, V Kandasamy, AS Begum
    Journal of Cybersecurity & Information Management 15 (1) , 2025
    2025
    Citations: 1
  • Strengthening of Cyber-Physical Infrastructure Resilience by DRL Adaptive Resource Management
    VVAA K. Swaminathan, R. Kiruba Buri, R. K. Harish
    Engineering Cyber-Physical Systems and Critical Infrastructures 20, 201–213 , 2026
    2026
  • Adaptive Intelligent Routing Algorithm for Integrating MANETs with IoT Networks
    AAGS K. Swaminathan, K. Dhanasekaran, R. Kiruba Buri
    CIS 2025. Lecture Notes in Networks and Systems, 1828, 248–259 , 2026
    2026
  • Optimizing Intrusion Detection Pipelines
    RK Buri, K Swaminathan, S Sundarsingh, K Sankar, P Yuvarajan
    Proceedings of International Conference on Communication and Computational … , 2026
    2026
  • Adaptive Adversarial-Resilient Explainable Intrusion Detection System for 6G Edge Networks (AARE-IDS)
    I Kiruba Buri R. (Department of CSE, School of Computing, Vel Tech ...
    Reconfigurable Intelligent Surfaces for 6G-Enabled Vehicle-to-Everything … , 2026
    2026
  • Optimizing Intrusion Detection Pipelines with AutoML: XGBoost for Enhanced Security
    PY R. Kiruba Buri , K. Swaminathan, S. Sundarsingh , K. Sankar
    Proceedings of International Conference on Communication and Computational … , 2026
    2026
  • Support Vector Machine-Lyapunov Control for Nonlinear Cluster Synchronization
    LB Dr.R.Kiruba buri, Dr. K. Swaminathan, Dr.A.Arivazhagi
    Soft Computing: Theories and Applications (SoCTA ) -2025, Soft Computing … , 2025
    2025
  • Block Chain-Reliable Incentive Cooperation System for Multiple Ways of Hopping Wireless Type of Network Infrastructure
    KSKK R. Kiruba Buri, Raunak Kumar )
    Ensuring Secure Connectivity Through AI-Powered Wireless Systems 8, 269-298. , 2025
    2025
  • Protecting Distributed Ledgers from Advanced Persistent Threats Using SVM-Based Blockchain Security
    KS R.Kiruba buri
    International Journal of communication and computer Technologies 13 (2), 11-17 , 2025
    2025
  • Trust Aware Water Strider Optimization-Based Clustering with Intrusion Detection in IoT Environments
    N Alzaben, M Maashi, M Alshammeri, V Saraswathi, RK Buri, SG Priya
    Tehnički vjesnik 32 (6), 2253-2260 , 2025
    2025
  • Hybrid Augmented Edge AI using Lightweight Knowledge Distillation and Incremental Learning with MobileNet for Real-Time Anomaly Detection in Resource-Constrained Smart Environments
    SB R.Kiruba Buri, K. Swaminathan, A.Arivazhagi
    7th International Conference on Communication and Intelligent Systems (ICCIS … , 2025
    2025
  • Adaptive Intelligent Routing Algorithm for Integrating MANETs with IoT Networks
    GS K. Swaminathan, K.Dhanasekaran, R.Kiruba Buri, A.Arivazhagi
    6th Congress on Intelligent Systems (CIS 2025), (IETE), Delhi Centre Soft … , 2025
    2025
  • Supply Chain Management Processing Device
    RB R.Kiruba buri, A.Arivazhagi, Swaminathan.K
    IN Patent CBR NO: 208,262 , 2025
    2025
  • NETWORKS AND SECURITY
    DSK Dr. Kiruba Buri R.
    ISBN: 978-93-6260-744-7 Charulatha Publications , 2025
    2025

Publications

Block Chain-Reliable Incentive Cooperation System for Multiple Ways of Hopping Wireless Type of Network Infrastructure. Kiruba Buri, R., Kumar, R., Swaminathan, K., & Kant, K. (2026).Source Title: Ensuring Secure Connectivity Through AI-Powered Wireless Systems (pp. 269-298). In V. Balas, H. Pandey, S. Mehta, V. Singh, & K. Joshi (Eds.), IGI Global Scientific Publishing. .


IoT-Driven Predictive Analytics for Smart Agriculture Using Random Forest Algorithm
R. Karthikeyan & K. Sankar R. Kiruba Buri, K. Swaminathan, S. Sundarsingh
Proceedings of International Conference on Information Technology and Artificial Intelligence (ITAI 2025)
LNNS,volume 1542, Pages:53-64
Publisher: SCOPUS Indexed Springer Book Series Lecture Notes in Networks and Systems

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Patents :
1. IoT thingsboard enabled smart position identification system for industrial accessories monitoring;
Patent number:202441011152
2. Supply Chain Management Processing Device; PAT: 455761-001