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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics
Palani Latha
Panimalar Engineering College
Computer Science & IT Electrical & Electronics Engineering

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Dynamic monitoring for enhancing QoS and security in distributed systems Sudhakar Periyasamy; Vijayalakshmi Alagarsamy; Palani Latha; Karuppiah Tamilarasi; Thenmozhi Elumalai; Prabu Kaliyaperumal
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp313-322

Abstract

Distributed systems are integral to modern digital infrastructure, supporting communication and data exchange across various sectors. Ensuring security while maintaining quality of service (QoS) in such environments presents a significant challenge. This study introduces a dynamic network monitoring system (DNMS) that incorporates adaptive monitoring mechanisms and dynamic security metrics to safeguard distributed systems. The proposed architecture utilizes an event analyzer (EA) to evaluate and classify system events based on criticality, enabling secure transmission decisions and efficient threat detection. Experimental evaluations demonstrate the DNMS achieves a low processing overhead of 12%, supports a high data handling capacity of 5,000 requests per second, and maintains a latency of just 150 milliseconds. Additionally, it ensures strong compliance with regulatory standards-achieving 95% alignment with GDPR and 97% with ISO 27001- and high threat detection accuracy, with 98% for phishing, 94% for malware, and 96% for insider threats. These results confirm the framework’s effectiveness in enhancing adaptive security, offering scalable and regulation-compliant solutions for complex distributed environments.
Similarity-driven intrusion detection for cloud-centric internet of things networks Jawahar Muthusamy; Palani Latha; Rajakumar Ponnumani; Anubhav Kumar; Rajakumar Perumal; Prabu Kaliyaperumal
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.13882

Abstract

Cloud-centric internet of things (IoT) environments continuously generates heterogeneous network traffic, making intrusion detection increasingly challenging as attack behaviors evolve beyond previously observed patterns. Conventional intrusion detection systems (IDS) rely primarily on supervised classification, limiting their ability to generalize to unseen threats while providing limited explanation for detection decisions. This paper proposes similarity-driven intrusion detection framework (SiamIDS), a SiamIDS that employs behavioral similarity learning to identify malicious network traffic. The framework integrates an autoencoder for feature compression with a Siamese bidirectional long short-term memory (Bi-LSTM) network for temporal similarity modeling, while SHapley Additive exPlanations (SHAP) provide feature-level explanations and ordering points to identify the clustering structure (OPTICS) supports post-detection behavioral analysis of anomalous traffic. The proposed framework was evaluated on the CIC IoT-DIAD 2024 dataset and compared with representative baseline deep learning models. Experimental results achieved an overall accuracy of 99.76%, precision of 95.62%, recall of 98.98%, and an F1-score of 97.24%, demonstrating consistent detection performance across seven attack families. Reconstruction loss analysis confirmed stable latent feature learning, while SHAP explanations showed that multiple network attributes collectively influenced intrusion decisions. These findings demonstrate that behavioral similarity learning provides an accurate and interpretable solution for intrusion detection in cloud-centric IoT environments.
Co-Authors Anubhav Kumar Jawahar Muthusamy Karuppiah Tamilarasi Prabu Kaliyaperumal Prabu Kaliyaperumal Rajakumar Perumal Rajakumar Ponnumani Sudhakar Periyasamy Thenmozhi Elumalai Vijayalakshmi Alagarsamy