Intrusion Detection Using the WEKA Machine Learning Tool
| dc.contributor.author | Sadiq, Ali | |
| dc.contributor.supervisor | Gulliver, T. Aaron | |
| dc.date.accessioned | 2021-12-21T01:39:14Z | |
| dc.date.available | 2021-12-21T01:39:14Z | |
| dc.date.copyright | 2021 | en_US |
| dc.date.issued | 2021-12-20 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Engineering M.Eng. | en_US |
| dc.description.abstract | The internet has become essential for data sharing, so it must be secure. Data encryption and authentication are not sufficient for internet security, and firewalls are unable to detect fragmented malicious packets. Further, attackers are constantly modifying their techniques and tools to produce devastating consequences such as productivity loss, financial loss and brand damage. Thus, it has become crucial to implement an efficient intrusion detection system, which is a very difficult challenge. The CSE-CIC-IDS2018 dataset consists of 14 network attacks and benign traffic with 80 features. In this report, data for seven network attacks and benign data are used. Principal Components Analysis (PCA) is employed to extract the ten most relevant features. The Machine Learning (ML) algorithms studied are Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) and Bayesian Network (BayesNet). The experiments are performed using the Waikato Environment for Knowledge Analysis (WEKA) tool with five-fold and ten-fold cross-validation. The performance measures employed are accuracy, precision, recall, F-measure, and execution time. The results obtained show that RF outperforms the other algorithms in accuracy, precision, recall, and F-measure. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/13619 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.title | Intrusion Detection Using the WEKA Machine Learning Tool | en_US |
| dc.type | project | en_US |