SIMULATING AND ANALYZING SIGNATURE-BASED NETWORK INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING TECHNIQUES
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| Main Authors: | , , |
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2022
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| _version_ | 1866902291286589440 |
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| author | Issah, Peter Paul Ganyo, Ransford Salam, Abdul Gaddafi |
| author_facet | Issah, Peter Paul Ganyo, Ransford Salam, Abdul Gaddafi |
| contents | <ul> <li><strong>Objective:</strong> The main objective of this study was to develop and evaluate intrusion detection models using machine learning algorithms to enhance security in data sharing over the internet.</li> <li><strong>Methods:</strong> The research utilized Weka Data Mining Software to create intrusion detection models based on the UNSW-NB15 dataset, which includes 44 attributes and 9 different types of attacks. Principal Components Analysis (PCA) was applied to reduce the number of features and improve detection speed. The machine learning methods evaluated were K-Nearest Neighbor (KNN), Random Forest (RF), Bayesian Network (BayesNet), and Decision Tree (J48).</li> <li><strong>Results:</strong> Experiments were conducted using the WEKA tool with ten-fold cross-validation, and performance was assessed based on execution time, F-measure, recall, accuracy, and precision. The Decision Tree (J48) algorithm outperformed other algorithms in terms of accuracy, precision, recall, and F-measure, while the Bayesian Network (BayesNet) algorithm produced the least accurate results.</li> <li><strong>Significance:</strong> This research provides a robust approach to enhancing intrusion detection systems, which is crucial for maintaining security and mitigating risks associated with internet data sharing. The findings indicate that the J48 algorithm is particularly effective for this purpose, offering a reliable tool for detecting intrusions and ensuring data integrity.</li> <li><strong>Keywords:</strong> Intrusion Detection, Machine Learning, Principal Components Analysis, UNSW-NB15 Dataset, WEKA, Security, Data Mining</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_12695351 |
| institution | Zenodo |
| language | eng |
| publishDate | 2022 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SIMULATING AND ANALYZING SIGNATURE-BASED NETWORK INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING TECHNIQUES Issah, Peter Paul Ganyo, Ransford Salam, Abdul Gaddafi Intrusion Detection, Machine Learning, Principal Components Analysis, UNSW-NB15 Dataset, WEKA, Security, Data Mining <ul> <li><strong>Objective:</strong> The main objective of this study was to develop and evaluate intrusion detection models using machine learning algorithms to enhance security in data sharing over the internet.</li> <li><strong>Methods:</strong> The research utilized Weka Data Mining Software to create intrusion detection models based on the UNSW-NB15 dataset, which includes 44 attributes and 9 different types of attacks. Principal Components Analysis (PCA) was applied to reduce the number of features and improve detection speed. The machine learning methods evaluated were K-Nearest Neighbor (KNN), Random Forest (RF), Bayesian Network (BayesNet), and Decision Tree (J48).</li> <li><strong>Results:</strong> Experiments were conducted using the WEKA tool with ten-fold cross-validation, and performance was assessed based on execution time, F-measure, recall, accuracy, and precision. The Decision Tree (J48) algorithm outperformed other algorithms in terms of accuracy, precision, recall, and F-measure, while the Bayesian Network (BayesNet) algorithm produced the least accurate results.</li> <li><strong>Significance:</strong> This research provides a robust approach to enhancing intrusion detection systems, which is crucial for maintaining security and mitigating risks associated with internet data sharing. The findings indicate that the J48 algorithm is particularly effective for this purpose, offering a reliable tool for detecting intrusions and ensuring data integrity.</li> <li><strong>Keywords:</strong> Intrusion Detection, Machine Learning, Principal Components Analysis, UNSW-NB15 Dataset, WEKA, Security, Data Mining</li> </ul> |
| title | SIMULATING AND ANALYZING SIGNATURE-BASED NETWORK INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING TECHNIQUES |
| topic | Intrusion Detection, Machine Learning, Principal Components Analysis, UNSW-NB15 Dataset, WEKA, Security, Data Mining |
| url | https://doi.org/10.5281/zenodo.12695351 |