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| dc.contributor.author | ABIY, BRAHANAMESKEL NEGA | |
| dc.date.accessioned | 2022-11-22T07:38:27Z | |
| dc.date.available | 2022-11-22T07:38:27Z | |
| dc.date.issued | 2022-07 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14500 | |
| dc.description.abstract | Distributed denial-of-service attack, also known as DDoS attack is one of the most dangerous cyber-security threats posed at present. The term itself describes the technique nicely. A distributed network of devices send junk information to a target to deny access to the target from its intended users. The mechanisms for building that traffic and sending vary, but the end goal is the same take the target down. DDoS makes a large amount of packet requests to be sent to the victim machine making it difficult for the defender to distinguish and protect itself from an attack. Meanwhile, legitimate users can’t access the recourse. Our proposed work aims to fill this gap by providing real time open-source robust system for DDoS attack detection and prediction which can be used by enterprise and industries to keep their networks and servers secure from malicious DDoS attacks. A Machine learning techniques is used to identify DDoS attacks and provides protection in a network with a maximum accuracy of 99.86%. Our paper uses machine learning techniques to select most adaptive features and a dynamic attribute selection techniques is used. We collected our own dataset and used only 1.1 Million packets from over all 32 GB of dataset. Our proposed machine learning for DDoS detection and classification technique is categorized into three modules. First is pre-processing in which features of the dataset are selected and normalized. In the second module we selected our feature which is the most correlated features of the dataset. This technique reduces the number of features from 10 to 1. In the last module, different classifier are used to classify DDoS and normal traffic. Artificial neural network (ANN), Decision Tree (DTs), Gradient Boosting, k-nearest neighbors (KNN), Logistic Regression, Naive Bayes, Gaussian NB and Random Forest classifiers are used in our research work. KNN, Decision Tree, Random Forest, Gradient Boosting, ANN, Gaussian NB has produced high attack detection rate greater than 99% with very low misclassified rate. Further, selected attributes are also classified on the basis of protocol ICMP, TCP and UDP. ANN has the highest attack detection rate with 99.86 % with very low misclassified rate. We demonstrate the effect of DDoS attack on the performance network using a test bed. We use ANN model to analyze the feasibility of DDoS attacks detection, keeping in view the attacker’s objective. Our analysis will help security experts to propose countermeasures. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | ELECTRICAL AND COMPUTER ENGINEERING | en_US |
| dc.title | Securing Enterprise Networks from DDoS Attacks Using Machine Learning Method | en_US |
| dc.type | Thesis | en_US |
