UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY
Effective intrusion detection systems (IDS) are becoming essential for maintaining computer network security due to the growing complexity of cyber-attacks. Machine Learning (ML) can increase the effectiveness of intrusion detection technology, which is an essential resource to safeguard network sec...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2024-03-01
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| Series: | Proceedings on Engineering Sciences |
| Subjects: | |
| Online Access: | https://pesjournal.net/journal/v6-n1/34.pdf |
| _version_ | 1797248113435475968 |
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| author | Rahul Kumar Sharma Arvind Kumar Pandey Bhuvana Jayabalan Preeti Naval |
| author_facet | Rahul Kumar Sharma Arvind Kumar Pandey Bhuvana Jayabalan Preeti Naval |
| author_sort | Rahul Kumar Sharma |
| collection | DOAJ |
| description | Effective intrusion detection systems (IDS) are becoming essential for maintaining computer network security due to the growing complexity of cyber-attacks. Machine Learning (ML) can increase the effectiveness of intrusion detection technology, which is an essential resource to safeguard network security. A novel ML technique for intrusion information detection called Stochastic Cat Swarm Optimized Privacy-Preserving Logistic Regression (SCSO-PPLR) is proposed. We assess intrusion detection systems using KDDCup99 dataset. The dataset is pre-processed using Z-score normalization to normalize the features. Next, Features are extracted by Principal Component Analysis (PCA). By comparing the results of the SCSO-PPLR methodology with traditional methods and using assessment criteria including accuracy, precision, recall, and F1-score, the model's performance is extensively evaluated. The study reveals that SCSO-PPLR is an acceptable strategy for intrusion detection in network security and it is effective. These insights broaden IDS and groundwork for further research on reliable cybersecurity remedies. |
| first_indexed | 2024-04-24T20:09:25Z |
| format | Article |
| id | doaj.art-f4eea0a4cd9241769880e1da3e25f2e6 |
| institution | Directory Open Access Journal |
| issn | 2620-2832 2683-4111 |
| language | English |
| last_indexed | 2024-04-24T20:09:25Z |
| publishDate | 2024-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj.art-f4eea0a4cd9241769880e1da3e25f2e62024-03-23T15:10:31ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112024-03-016131132010.24874/PES.SI.24.02.014UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITYRahul Kumar Sharma 0https://orcid.org/0000-0003-1604-6962Arvind Kumar Pandey 1https://orcid.org/0000-0001-5294-0190Bhuvana Jayabalan 2https://orcid.org/0000-0002-8372-6311Preeti Naval 3https://orcid.org/0000-0003-2988-7082Noida Institute of Engineering & Technology, Greater Noida, Uttar Pradesh, India Arka Jain University, Jamshedpur, Jharkhand, India Jain (Deemed to be University), Bangalore, Karnataka, India Maharishi University of Information Technology, Uttar Pradesh, India Effective intrusion detection systems (IDS) are becoming essential for maintaining computer network security due to the growing complexity of cyber-attacks. Machine Learning (ML) can increase the effectiveness of intrusion detection technology, which is an essential resource to safeguard network security. A novel ML technique for intrusion information detection called Stochastic Cat Swarm Optimized Privacy-Preserving Logistic Regression (SCSO-PPLR) is proposed. We assess intrusion detection systems using KDDCup99 dataset. The dataset is pre-processed using Z-score normalization to normalize the features. Next, Features are extracted by Principal Component Analysis (PCA). By comparing the results of the SCSO-PPLR methodology with traditional methods and using assessment criteria including accuracy, precision, recall, and F1-score, the model's performance is extensively evaluated. The study reveals that SCSO-PPLR is an acceptable strategy for intrusion detection in network security and it is effective. These insights broaden IDS and groundwork for further research on reliable cybersecurity remedies.https://pesjournal.net/journal/v6-n1/34.pdfmachine learningnetwork securitynetwork attackcybersecurityintrusion detection systems (ids)stochastic cat swarm optimized privacy-preserving logistic regression (scso-pplr) |
| spellingShingle | Rahul Kumar Sharma Arvind Kumar Pandey Bhuvana Jayabalan Preeti Naval UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY Proceedings on Engineering Sciences machine learning network security network attack cybersecurity intrusion detection systems (ids) stochastic cat swarm optimized privacy-preserving logistic regression (scso-pplr) |
| title | UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY |
| title_full | UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY |
| title_fullStr | UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY |
| title_full_unstemmed | UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY |
| title_short | UTILIZING MACHINE LEARNING-BASED INTRUSION DETECTION TECHNOLOGIES FOR NETWORK SECURITY |
| title_sort | utilizing machine learning based intrusion detection technologies for network security |
| topic | machine learning network security network attack cybersecurity intrusion detection systems (ids) stochastic cat swarm optimized privacy-preserving logistic regression (scso-pplr) |
| url | https://pesjournal.net/journal/v6-n1/34.pdf |
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