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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Kaliyaperumal, Prabu
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Computer Science & IT Electrical & Electronics Engineering

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Harnessing DBSCAN and auto-encoder for hyper intrusion detection in cloud computing Kaliyaperumal, Prabu; Periyasamy, Sudhakar; Periyasamy, Muthusamy; Alagarsamy, Abinaya
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8135

Abstract

The widespread availability of internet services has led to a surge in network attacks, raising serious concerns about cybersecurity. Intrusion detection systems (IDS) are pivotal in safeguarding networks by identifying malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, probe, remote-to-local, and user-to-root attacks. To counter these threats effectively, this research focuses on utilizing unsupervised learning to train detection models. The proposed method involves employing auto-encoders (AE) for attack detection and density-based spatial clustering of applications with noise (DBSCAN) for attack clustering. By using preprocessed and unlabeled normal network traffic data, the approach enables the identification of unknown attacks while minimizing the impact of imbalanced training data on model performance. The auto-encoder method utilizes the reconstruction error as an anomaly detection metric, while DBSCAN employs a density-based approach to identify clusters, manage noise, accommodate diverse shapes, automatically determine cluster count, ensure scalability, and minimize false positives. Tested on standard datasets such as KDDCup99, UNSW-NB15, CICIDS2017, and CSE-CIC-IDS2018, this proposed model achieves accuracies exceeding 98.36%, 98.22%, 98.45%, and 98.51%, respectively. These results demonstrate the effectiveness of unsupervised learning in detecting and clustering novel intrusions while managing imbalanced data.
Enhancing network security using unsupervised learning approach to combat zero-day attack Perumal, Rajakumar; Karuppiah, Tamilarasi; Panneerselvam, Uppiliraja; Annamalai, Venkatesan; Kaliyaperumal, Prabu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1284-1293

Abstract

Machine learning (ML) and advanced neural network methodologies like deep learning (DL) techniques have been increasingly utilized in developing intrusion detection systems (IDS). However, the growing quantity and diversity of cyber-attacks pose a significant challenge for IDS solutions reliant on historical attack signatures. This highlights the industry's need for resilient IDSs that can identify zero-day attacks. Current studies focusing on outlier-based zero-day detection are hindered by elevated false-negative rates, thereby constraining their practical efficacy. This paper suggests utilizing an autoencoder (AE) approach for zero-day attack detection, aiming to achieve high recall while minimizing false negatives. Evaluation is conducted using well-established IDS datasets, CICIDS2017 and CSECICIDS2018. The model's efficacy is demonstrated by contrasting its performance with that of a one-class support vector machine (OCSVM). The research underscores the OCSVM's capability in distinguishing zero-day attacks from normal behavior. Leveraging the encoding-decoding capabilities of AEs, the proposed model exhibits promising results in detecting complex zero-day attacks, achieving accuracies ranging from 93% to 99% across datasets. Finally, the paper discusses the balance between recall and fallout, offering valuable insights into model performance.
A framework for dynamic monitoring of distributed systems featuring adaptive security Periyasamy, Sudhakar; Kaliyaperumal, Prabu; Alagarsamy, Abinaya; Elumalai, Thenmozhi; Karuppiah, Tamilarasi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp660-669

Abstract

Distributed systems play a crucial role in today’s information-based society, enabling seamless communication among governmental, industrial, social, and non-governmental institutions. As information becomes increasingly complex, the software industry is highly concerned about the heterogeneity and dynamicity of distributed systems. It is common for various types of information and services to be disseminated on different sites, especially in web 2.0. Since ‘information’ has become a prime tool for organizations to achieve their vision and mission, a high level of quality of service (QoS) is mandatory to disseminate and access information and services over remote sites, despite an unsecure communication system. These systems are expected to have security mechanisms in place, render services within an acceptable response time, dynamically adapt to environmental requirements, and secure key information. This research article proposes a framework for evaluating and determining a threshold up to which distributed systems can collect data to adapt to the environment. The study also proposes a dynamic security metric to determine the level of security disturbance caused by the monitoring system for adaptation and the measures to be implemented. Additionally, the paper details the role of the monitoring system in safeguarding the adaptive distributed system and proposes an adaptive monitoring system that can modify its functionality as per the environment.
A hybrid framework for enhanced intrusion detection in cloud environments leveraging autoencoder Alagarsamy, Abinaya; Elumalai, Thenmozhi; Ramesh, S. P.; Karuppiah, Tamilarasi; Kaliyaperumal, Prabu; Perumal, Rajakumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp555-564

Abstract

In today’s world, the significance of network security and cloud environments has grown. The rising demand for data transmission, along with the versatility of cloud-based solutions and widespread availability of global resources, are key drivers of this growth. In response to rapidly evolving threats and malicious attacks, developing a robust intrusion detection system (IDS) is essential. This study addresses the imbalanced data and utilizes an unsupervised learning approach to protect network data. The suggested hybrid framework employs the CIC-IDS2017 dataset, integrating methods for handling imbalanced data with unsupervised learning to enhance security. Following preprocessing, principal component analysis (PCA) reduces the dimensionality from eighty features to twenty-three features. The extracted features are input into density-based spatial clustering of applications with noise (DBSCAN), a clustering algorithm. particle swarm optimization (PSO) optimizes DBSCAN, grouping similar traffic and enhancing classification. To address the imbalances in the learning process, the autoencoder (AE) algorithm demonstrates unsupervised learning. The data from the cluster is input into the AE, a deep learning algorithm, which classifies traffic as normal or an attack. The proposed approach (PCA+DBSCAN+AE) attains remarkable intrusion detection accuracy exceeding 98%, and outperforms five contemporary methodologies.
Adaptive intrusion detection system with DBSCAN to enhance banking cybersecurity Periyasamy, Sathiyaseelan; Kumar, Anubhav; Muthulakshmi, Karupusamy; Elumalai, Thenmozhi; Kaliyaperumal, Prabu; Perumal, Rajakumar
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.pp247-256

Abstract

The accelerating pace of digital transformation in the banking sector has highlighted the critical need for comprehensive cybersecurity strategies capable of countering evolving cyber threats. This study introduces an innovative intrusion detection framework tailored for banking environments, leveraging the CICIDS2017 and CSECICIDS2018 datasets for evaluation and validation. The proposed framework integrates data preprocessing, feature reduction, and advanced attack detection methods to enhance detection accuracy. A basic autoencoder is utilized for dimensionality reduction, streamlining input data while preserving essential attributes. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is then applied for attack detection, enabling the detection of intricate attack patterns and their classification into specific attack groups. The proposed adaptive intrusion detection system (IDS) framework demonstrates outstanding performance, achieving precision, recall, F1-score, and accuracy rates exceeding 98%. Comparative evaluations against conventional techniques, such as support vector machines (SVM), long short-term memory (LSTM), and K-means, highlight its superiority in terms accuracy and computational efficiency. This research address key challenges, including high-dimensional datasets, class imbalance, and dynamic threat landscapes, offering a scalable and efficient solution to enhance the security of banking operations and enable proactive threat mitigation in the sector.
DeepRetina: a multimodal framework for early diabetic retinopathy detection and progression prediction Ramasamy, Sunder; Mohanraj, Brindha; Pushpanathan, Sridhar; Elumalai, Thenmozhi; Kaliyaperumal, Prabu; Perumal, Rajakumar
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.pp152-160

Abstract

Diabetic retinopathy (DR) remains one of the top causes of vision loss globally, and early detection and accurate progression prediction are critical in its management. This paper introduces DeepRetina, a deep learning framework that integrates state-of-the-art multimodal retinal imaging techniques with patient-specific clinical data for the improved diagnosis and prognosis of DR. DeepRetina harnesses cutting-edge convolutional neural networks (CNNs) and attention mechanisms to jointly analyze optical coherence tomography (OCT) scans and fundus photographs. The architecture further includes a temporal module that investigates the longitudinal changes in the retina. DeepRetina fuses these heterogeneous data sources with patient clinical information in pursuit of early detection of DR and provides personalized predictions for the progression of the disease. We use a specially designed CNN architecture to process high-resolution retinal images, coupled with a self-attention mechanism that focuses on the most relevant features. This recurrent neural network (RNN) module empowers it to integrate time-series data that captures the evolution of retinal abnormalities. Another neural network branch considering patientspecific clinical data, such as demographic information, medical history, and laboratory test results, was taken into account and concatenated with the imaging features for a holistic analysis. DeepRetina achieved 95% sensitivity, 98% specificity for early DR detection, and a 0.92 area under the curve (AUC) for 5-year progression prediction, outperforming existing methods.
Enhanced smart farming security with class-aware intrusion detection in fog environment Palanisamy, Selvaraj; Rajamani, Radhakrishnan; Pramasivam, Prabakaran; Sumithra, Mani; Kaliyaperumal, Prabu; Perumal, Rajakumar
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.pp257-266

Abstract

The adoption of the internet of things (IoT) in smart farming has enabled real-time data collection and analysis, leading to significant improvements in productivity and quality. However, incorporating diverse sensors across large-scale IoT systems creates notable security challenges, particularly in dynamic environments like Fog-to-Things architectures. Threat actors may exploit these weaknesses to disrupt communication systems and undermine their integrity. Tackling these issues necessitates an intrusion detection system (IDS) that achieves a balance between accuracy, resource optimization, compatibility, and affordability. This study introduces an innovative deep learning-driven IDS tailored for fog-assisted smart farming environments. The proposed system utilizes a class-aware autoencoder for detecting anomalies and performing initial binary classification, with a SoftMax layer subsequently employed for multi-class attack categorization. The model effectively identifies various threats, such as distributed denial of service (DDoS), ransomware, and password attacks, while enhancing security performance in environments with limited resources. By utilizing the Fog-to-Things architecture, the proposed IDS guarantees reliable and low-latency performance under extreme environmental conditions. Experimental results on the TON_IoT dataset reveal excellent performance, surpassing 98% accuracy in both binary and multi-class classification tasks. The proposed model outperforms conventional models (convolutional neural network (CNN), recurrent neural network (RNN), deep neural network (DNN), and gated recurrent unit (GRU)), highlighting its superior accuracy and effectiveness in securing smart farming networks.
Dynamic monitoring for enhancing QoS and security in distributed systems Periyasamy, Sudhakar; Alagarsamy, Vijayalakshmi; Latha, Palani; Tamilarasi, Karuppiah; Elumalai, Thenmozhi; Kaliyaperumal, Prabu
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.
Co-Authors Alagarsamy, Abinaya Alagarsamy, Vijayalakshmi Annamalai, Venkatesan Elumalai, Thenmozhi Karuppiah, Tamilarasi Kumar, Anubhav Latha, Palani Mohanraj, Brindha Muthulakshmi, Karupusamy Palanisamy, Selvaraj Panneerselvam, Uppiliraja Periyasamy, Muthusamy Periyasamy, Sathiyaseelan Periyasamy, Sudhakar Perumal, Rajakumar Pramasivam, Prabakaran Pushpanathan, Sridhar Rajamani, Radhakrishnan Ramasamy, Sunder Ramesh, S. P. Sumithra, Mani Tamilarasi, Karuppiah