Distributed detection of ddos attacks in machine learning-enabled software defined networks
Başlık çevirisi mevcut değil.
- Tez No: 797235
- Danışmanlar: PROF. FRANCESCO MUSUMECİ, PROF. MASSİMO TORNATORE
- Tez Türü: Yüksek Lisans
- Konular: Elektrik ve Elektronik Mühendisliği, Biyomühendislik, Electrical and Electronics Engineering, Bioengineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2019
- Dil: İngilizce
- Üniversite: Polıtecnıco Dı Mılano
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
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Özet (Çeviri)
Software Defined Networking (SDN) provides several advantages compared to traditional network architectures as it enables efficient equipment control, high reconfigurability and especially network automation. However, due to its centralized vision, SDN controllers are regarded as vulnerable victims of cyber attacks, such as Distributed Denial of Service (DDoS), that may hinder proper controller functioning by exhausting its resources with malicious packets. Because of this, mitigation of DDoS attacks at a close point to the real victim (e.g. servers) does not remove the potential threat for the SDN structure. If SDN controller becomes unavailable, the entire network behavior can be affected and so also the transport of legitimate traffic. To address this issue, early detection of DDoS attacks can be implemented by leveraging data plane programmability and exploiting efficient DDoS attacks detection mechanisms as enabled by machine learning algorithms. A distributed detection architecture can be defined as programmable switches run ML-classifiers in order to classify ongoing traffic as attack or legitimate and the DDoS attacks can be mitigated by blocking the detected attack traffic. In this thesis we develop different ML models to perform in-network DDoS attacks detection. Using various ML algorithms, we compare different DDoS attacks detection architectures, namely distributed and centralized, in terms of classification accuracy and under different network scenarios. We found that the ML-assisted DDoS attacks detection developed in this thesis can be performed in around 90 µs providing up to 99.89% accuracy even under low attack rates. Moreover, for the considered scenarios, the duration of training is always below 10.4 s for all ML algorithms.
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