DDOS ATTACKS DETECTION IN IOT USING CLASSIFICATION ALGORITHM
| dc.contributor.author | Gabli, Nadjet | |
| dc.contributor.author | Cheikh ,Mohamed | |
| dc.date.accessioned | 2025-04-17T07:14:37Z | |
| dc.date.available | 2025-04-17T07:14:37Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Distributed denial-of-service (DDoS) attack, is one of the most common IoT attacks . With the rapid development of computer and communication technology, the harm of DDoS attack is becoming more and more serious. Therefore, the research on DDoS attack detection becomes more important. In view of this, in this research we will select the best DDoS attack detection supervised techniques from the seven techniques K_Nearest_Neighbors (K-NN), super vector machine (SVM), naïve bayes (NB), decision tree (REPTree), random forest (RF) , decision tree(J48) and Multilayer Perceptron (MLP) ,using the CICDDoS2019 dataset and deep learning software WEKA tool for implementing the ML .The experimental results shown that the proposed DDoS attack detection method based on machine learning has a good detection rate for the current popular DDoS attack. And the random forest classification algorithm is the best CICDDoS2019 dataset classification algorithm with 99.99% detection. | |
| dc.identifier.uri | http://dspace.univ-skikda.dz:4000/handle/123456789/4583 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Sciences | |
| dc.title | DDOS ATTACKS DETECTION IN IOT USING CLASSIFICATION ALGORITHM | |
| dc.title.alternative | Network and Distributed System | |
| dc.type | Masters degree thesis |