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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.
Co-Authors Alagarsamy, Abinaya Annamalai, Venkatesan Elumalai, Thenmozhi Karuppiah, Tamilarasi Panneerselvam, Uppiliraja Periyasamy, Muthusamy Periyasamy, Sudhakar Perumal, Rajakumar Ramesh, S. P.