DDoS Attacks Detection using Machine Learning
| dc.contributor.author | Sabir, Mohammed Younus | |
| dc.contributor.supervisor | Gebali, Fayez | |
| dc.date.accessioned | 2023-04-28T17:07:43Z | |
| dc.date.available | 2023-04-28T17:07:43Z | |
| dc.date.copyright | 2023 | en_US |
| dc.date.issued | 2023-04-28 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering M.Eng. | en_US |
| dc.description.abstract | The advancement in information technology has created a new era named as Internet of Things (IoT). This new technology has allowed things to be connected to the Internet, for example smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend has enhanced the lifestyle of the users of these devices, and it provides new services and applications to them. The fast growth of IOT has resulted in inclusion and connection of these devices a predominant procedure. Though there are many advantages due to usage of IoT devices, there are different challenges as well due to its usage. Among the many existing challenges, Distributed Denial of Service(DDoS) attack is a relatively simple but very powerful technique to attack intranet and Internet resources. Usually, in this attack, the legitimate users are deprived of using web-based services by many compromised machines. DDoS attacks can be implemented in network, transport and application layers using different protocols, such as TCP, UDP, ICMP and HTTP. The CIC-DDoS2019 dataset consists of 11 different DDoS attacks and benign traffic with 88 features. In this report, data for six DDoS attacks and benign data has been used. Info Gain Attribute Evaluator was used to extract the twenty-four most important features. The Machine Learning (ML) algorithms studied are Bayesian Network (BayesNet) , K-Nearest Neighbors (KNN) , J48. The experiments have been performed using the Waikato Environment for Knowledge Analysis (WEKA) tool with five-fold validation. Accuracy, Precision, Recall, F-measure, and execution time have been used as the performance metrics. From the results obtained, J48 performed better among all the algorithms in terms of accuracy, precision, recall and F-measure. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/15046 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.title | DDoS Attacks Detection using Machine Learning | en_US |
| dc.type | project | en_US |