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1. Causal Analysis for Software-Defined Networking Attacks

   Ujcich, Benjamin E. ; Jero, Samuel ; Skowyra, Richard ; Bates, Adam ; Sanders, William H. ; Okhravi, Hamed ( July 2021 , USENIX Security Symposium)

   null (Ed.)

   Software-defined networking (SDN) has emerged as a flexible network architecture for central and programmatic control. Although SDN can improve network security oversight and policy enforcement, ensuring the security of SDN from sophisticated attacks is an ongoing challenge for practitioners. Existing network forensics tools attempt to identify and track such attacks, but holistic causal reasoning across control and data planes remains challenging. We present PicoSDN, a provenance-informed causal observer for SDN attack analysis. PicoSDN leverages fine-grained data and execution partitioning techniques, as well as a unified control and data plane model, to allow practitioners to efficiently determine root causes of attacks and to make informed decisions on mitigating them. We implement PicoSDN on the popular ONOS SDN controller. Our evaluation across several attack case studies shows that PicoSDN is practical for the identification, analysis, and mitigation of SDN attacks. 

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2. An SVM Based DDoS Attack Detection Method for Ryu SDN Controller

   https://doi.org/10.1145/3360468.3368183  

   Mehr, Shideh Yavary ; Ramamurthy, Byrav ( December 2019 , CoNEXT ’19 Companion)

   Software-Defined Networking (SDN) is a dynamic, and manageable network architecture which is more cost-effective than existing network architectures. The idea behind this architecture is to centralize intelligence from the network hardware and funnel this intelligence to the management system (controller) [2]-[4]. Since the centralized SDN controller controls the entire network and manages policies and the flow of the traffic throughout the network, it can be considered as the single point of failure [1]. It is important to find some ways to identify different types of attacks on the SDN controller [8]. Distributed Denial of Service (DDoS) attack is one of the most dangerous attacks on SDN controller. In this work, we implement DDoS attack on the Ryu controller in a tree network topology using Mininet emulator. Also, we use a machine learning method, Vector Machines (SVM) to detect DDoS attack. We propose to install flows in switches, and we consider time attack pattern of the DDoS attack for detection. Simulation results show the effects of DDoS attacks on the Ryu controller is reduced by 36% using our detection method. 

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3. Distributed Security Network Functions against Botnet Attacks in Software-defined Networks

   https://doi.org/10.1109/NFV-SDN.2018.8725657  

   Park, Younghee ; Kengalahalli, Nikhil Vijayakumar ; Chang, Sang-Yoon ( November 2018 , IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN))

   For the past decade, botnets have dominated network attacks in spite of significant research advances in defending against them. The distributed attack sources, the network size, and the diverse botnet attack techniques challenge the effectiveness of a single-point centralized security solution. This paper proposes a distributed security system against large-scale disruptive botnet attacks by using SDN/NFV and machine-learning. In our system, a set of distributed network functions detect network attacks for each protocol and to collect real-time traffic information, which also gets relayed to the SDN controller for more sophisticated analyses. The SDN controller then analyzes the real-time traffic with the only forwarded information using machine learning and updates the flow rule or take routing/bandwidth-control measures, which get executed on the nodes implementing the security network functions. Our evaluations show the proposed system to be an efficient and effective defense method against botnet attacks. The evaluation results demonstrated that the proposed system detects large-scale distributed network attacks from botnets at the SDN controller while the network functions locally detect known attacks across different networking protocols. 

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4. Distributed Security Network Functions against Botnet Attacks in Software-defined Networks

   Park, Younghee Kengalahalli ( November 2018 , IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN))

   For the past decade, botnets have dominated network attacks in spite of significant research advances in defending against them. The distributed attack sources, the network size, and the diverse botnet attack techniques challenge the effectiveness of a single-point centralized security solution. This paper proposes a distributed security system against largescale disruptive botnet attacks by using SDN/NFV and machinelearning. In our system, a set of distributed network functions detect network attacks for each protocol and to collect real-time traffic information, which also gets relayed to the SDN controller for more sophisticated analyses. The SDN controller then analyzes the real-time traffic with the only forwarded information using machine learning and updates the flow rule or take routing/bandwidth-control measures, which get executed on the nodes implementing the security network functions. Our evaluations show the proposed system to be an efficient and effective defense method against botnet attacks. The evaluation results demonstrated that the proposed system detects large-scale distributed network attacks from botnets at the SDN controller while the network functions locally detect known attacks across different networking protocols. 

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5. Hierarchical Classic Controllers (HCC) with an Enhanced SVM Method for DDoS Attack Detection in SDN

   https://doi.org/10.1109/ANTS56424.2022.10227717  

   Mehr, Shideh Yavary ; Ramamurthy, Byrav ( December 2022 , 2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS))

   A centralized Software-defined Network (SDN) controller, due to its nature, faces many issues such as a single point of failure, computational complexity growth, different types of attacks, reliability challenges and scalability concerns. One of the most common fifth generation cyber-attacks is the Distributed Denial of Service (DDoS) attack. Having a single SDN controller can lead to a plethora of issues with respect to latency, computational complexity in the control plane, reachability, and scalability as the network scale increases. To address these issues, state-of-the-art approaches have investigated multiple SDN controllers in the network. The placement of these multiple controllers has drawn more attention in recent studies. In our previous work, we evaluated an Entropy-based technique and a machine learning-based Support Vector Machine (SVM) to detect DDoS using a single SDN controller. In this paper, we extend our previous work to further decrease the impact of the DDoS attacks on the SDN controller. Our new technique called Hierarchical Classic Controllers (HCC) uses SVM and Entropy methods to detect abnormal traffic which can lead to network failures caused by overwhelming a single controller. Determining the number of controllers and their best placement are major contributions in our new method. Our results show that the combination of the above three methods (HCC with SVM and Entropy), in the case of a network with 3 controllers provides greater accuracy and improves the DDoS attack detection rate to 86.12% compared to 79.03% and 81.33% using Entropy-based HCC and SVM-based HCC, respectively. 

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