Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
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Article Number | 01086 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/e3sconf/202339101086 | |
Published online | 05 June 2023 |
New Intrusion Detection System Based on Neural Networks and Clustering
1 Department of Computer Science and Engineering, Annamalai University
2 Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology
* Corresponding author: samata.kancherla@gmail.com
Efficiency of Intrusion detection systems-IDS are evaluated using parameters like completeness, performance and accuracy. The first important parameter is the completeness, which occurs when the detection of attack fails. This is the most difficult parameter to evaluate compared to the other two parameters. The second one is performance, which indicates the audit events process. When the IDS doesn’t work properly or works poorly, the real time detection becomes impossible. Legitimate actions are flagged as anomalous which is termed as inaccuracy. This part needs attention to address the inaccuracies. Optimal solutions must take the inaccuracies into consideration for accuracy, thereby efficiency of IDS. There are different trends in IDS. Some of them are discussed below. Behavior and knowledge-based IDS: Misuse detection, appearance-based detection, behavior detection and anomaly detection etc. There are numerous stability and security issues as a result of the Internet’s and computer networks’ rapid proliferation. The present study reports the case study of image processing in a fruit grading plant with data safety over cloud with Original Equipment Manufacturer (OEM). How Artificial Neural Networks (ANN) architecture can help is discussed and recommendations are made for impending improvement.
Key words: Network Artificial Neural Networks / Optimal Intrusion detection / Cloud / Safety of image processing data / Case study of Fruit images
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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