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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Periyasamy, Muthusamy
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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.
Network routing and scheduling architecture in a fully distributed cloud computing environment Kumar S, Vijaya; Periyasamy, Muthusamy; Radhakrishnan, R.; Karuppiah, Tamilarasi; Elumalai, Thenmozhi
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.pp1242-1252

Abstract

Distributed computing has turned into an indispensable application administration because of the colossal development and fame of the internet. However, determining the allocation of various tasks to suitable service nodes is crucial. For the reasons expressed over, an effective booking strategy is expected to work on the framework’s exhibition. As a result, three-layer cloud dispatching (TLCD) design is introduced to further develop mission planning execution. The assignments should be arranged into various sorts in the primary layer in radiance of about their personalities clustering selection algorithm is composed of then recommended in second layer towards dispatch the undertakings to significant help bunches. Likewise, to further develop booking effectiveness, another planning technique for third stage proposes dispatching that job here to system thinking in a central server. As a rule, the proposed TLCD design yields the quickest work finishing time. Moreover, in cloud computing network architecture, load balancing and stability can be achieved.
Co-Authors Alagarsamy, Abinaya Elumalai, Thenmozhi Kaliyaperumal, Prabu Karuppiah, Tamilarasi Kumar S, Vijaya Periyasamy, Sudhakar Radhakrishnan, R.