Abstract
In the traditional network system, control plane (CP) and data plane (DP) are both located on the same network device. If that device fails, the whole system will stop working and the traffics cannot be managed by the administrator. In distributed denial of service (DDoS) or denial of service (DoS), the attackers usually use botnets to generate a large or medium size traffic flow toward the server where the service is active. Nowadays, many techniques have been proposed to detect DoS, DDoS attacks but they are not very effective. However, the separation of CP and DP in software-defined networking (SDN) provides a good foundation for attack detection and prevention no matter whether the attackers attack some parts of the SDN or all of them. In this research, we use entropy to calculate the randomness level of the size of the packet to CP, the system’s brain. With this method, the controller can handle faster and we can get more correct output compared with the machine learning method in the SDN.
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Truong, DT., Tran, KD., Nguyen, QB., Tran, DT. (2021). Detection of DoS, DDoS Attacks in Software-Defined Networking. In: Kumar, R., Quang, N.H., Kumar Solanki, V., Cardona, M., Pattnaik, P.K. (eds) Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-7527-3_3
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DOI: https://doi.org/10.1007/978-981-15-7527-3_3
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