New Intrusion Detection System Based on Neural Networks and Clustering
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 par...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01086.pdf |
| _version_ | 1797807819800444928 |
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| author | Samata Kancherla Raman Dugyala Saravanan S. Saminathan R. |
| author_facet | Samata Kancherla Raman Dugyala Saravanan S. Saminathan R. |
| author_sort | Samata Kancherla |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:28:21Z |
| format | Article |
| id | doaj.art-e4cd8ebd1f1543ffb07127f068dbf3fb |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:28:21Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-e4cd8ebd1f1543ffb07127f068dbf3fb2023-06-09T09:12:17ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013910108610.1051/e3sconf/202339101086e3sconf_icmed-icmpc2023_01086New Intrusion Detection System Based on Neural Networks and ClusteringSamata Kancherla0Raman Dugyala1Saravanan S.2Saminathan R.3Department of Computer Science and Engineering, Annamalai UniversityDepartment of Computer Science and Engineering, Chaitanya Bharathi Institute of TechnologyDepartment of Computer Science and Engineering, Annamalai UniversityDepartment of Computer Science and Engineering, Annamalai UniversityEfficiency 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01086.pdfnetwork artificial neural networksoptimal intrusion detectioncloudsafety of image processing datacase study of fruit images |
| spellingShingle | Samata Kancherla Raman Dugyala Saravanan S. Saminathan R. New Intrusion Detection System Based on Neural Networks and Clustering E3S Web of Conferences network artificial neural networks optimal intrusion detection cloud safety of image processing data case study of fruit images |
| title | New Intrusion Detection System Based on Neural Networks and Clustering |
| title_full | New Intrusion Detection System Based on Neural Networks and Clustering |
| title_fullStr | New Intrusion Detection System Based on Neural Networks and Clustering |
| title_full_unstemmed | New Intrusion Detection System Based on Neural Networks and Clustering |
| title_short | New Intrusion Detection System Based on Neural Networks and Clustering |
| title_sort | new intrusion detection system based on neural networks and clustering |
| topic | network artificial neural networks optimal intrusion detection cloud safety of image processing data case study of fruit images |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01086.pdf |
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