Classification model for accuracy and intrusion detection using machine learning approach
In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-04-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-437.pdf |
| _version_ | 1818849498261094400 |
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| author | Arushi Agarwal Purushottam Sharma Mohammed Alshehri Ahmed A. Mohamed Osama Alfarraj |
| author_facet | Arushi Agarwal Purushottam Sharma Mohammed Alshehri Ahmed A. Mohamed Osama Alfarraj |
| author_sort | Arushi Agarwal |
| collection | DOAJ |
| description | In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach. |
| first_indexed | 2024-12-19T06:34:11Z |
| format | Article |
| id | doaj.art-35062120d2ef48efa85efe4b837fe175 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-19T06:34:11Z |
| publishDate | 2021-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-35062120d2ef48efa85efe4b837fe1752022-12-21T20:32:15ZengPeerJ Inc.PeerJ Computer Science2376-59922021-04-017e43710.7717/peerj-cs.437Classification model for accuracy and intrusion detection using machine learning approachArushi Agarwal0Purushottam Sharma1Mohammed Alshehri2Ahmed A. Mohamed3Osama Alfarraj4Amity School of Engineering and Technology, Amity University, Uttar Pradesh, IndiaAmity School of Engineering and Technology, Amity University, Uttar Pradesh, IndiaDepartment of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi ArabiaDepartment of Computer Science, Community College, King Saud University, Riyadh, Saudi ArabiaIn today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.https://peerj.com/articles/cs-437.pdfIntrusion detection systemK-Nearest Neighbors (KNN)Support vector machine (SVM)Naive Bayes (NB)UNSWNB15 dataset |
| spellingShingle | Arushi Agarwal Purushottam Sharma Mohammed Alshehri Ahmed A. Mohamed Osama Alfarraj Classification model for accuracy and intrusion detection using machine learning approach PeerJ Computer Science Intrusion detection system K-Nearest Neighbors (KNN) Support vector machine (SVM) Naive Bayes (NB) UNSWNB15 dataset |
| title | Classification model for accuracy and intrusion detection using machine learning approach |
| title_full | Classification model for accuracy and intrusion detection using machine learning approach |
| title_fullStr | Classification model for accuracy and intrusion detection using machine learning approach |
| title_full_unstemmed | Classification model for accuracy and intrusion detection using machine learning approach |
| title_short | Classification model for accuracy and intrusion detection using machine learning approach |
| title_sort | classification model for accuracy and intrusion detection using machine learning approach |
| topic | Intrusion detection system K-Nearest Neighbors (KNN) Support vector machine (SVM) Naive Bayes (NB) UNSWNB15 dataset |
| url | https://peerj.com/articles/cs-437.pdf |
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