| Title | Slow Hypertext Transfer Protocol Mitigation Model in Software Defined Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Abisoye, Opeyemi Aderiike, Shadrach Akanji, Oluwatobi, Abisoye, Blessing Olatunde, Awotunde, Joseph |
| Conference Name | 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) |
| Date Published | Oct. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-9675-6 |
| Keywords | composability, Computer crime, DDoS attack mitigation, DDOS attacks detection, denial-of-service attack, genetic algorithm, genetic algorithms, Human Behavior, IP networks, Metrics, Protocols, pubcrawl, resilience, Resiliency, slow DDoS mitigation, slow distributed denial of service, Software Defined Network, support vector machine, Support vector machines, Web servers |
| Abstract | Distributed Denial of Service (DDoS) attacks have been one of the persistent forms of attacks on information technology infrastructure connected to a public network due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS attacks which overwhelms the target with large number of request packets. However, compared to volumetric DDoS, low amount of research has been executed on mitigating slow DDoS. Data mining approaches and various Artificial Intelligence techniques have been proved by researchers to be effective for reduce DDoS attacks. This paper provides the scholarly community with slow DDoS attack detection techniques using Genetic Algorithm and Support Vector Machine aimed at mitigating slow DDoS attack in a Software-Defined Networking (SDN) environment simulated in GNS3. Genetic algorithm was employed to select the features which indicates the presence of an attack and also determine the appropriate regularization parameter, C, and gamma parameter for the Support Vector Machine classifier. Results obtained shows that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. Also, the algorithm for subsequent implementation of the selective adaptive bubble burst mitigation mechanism was presented. |
| URL | https://ieeexplore.ieee.org/document/9325601 |
| DOI | 10.1109/ICDABI51230.2020.9325601 |
| Citation Key | abisoye_slow_2020 |
Slow Hypertext Transfer Protocol Mitigation Model in Software Defined Networks