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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Causal Analysis for Software-Defined Networking Attacks
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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- Award ID(s):
- 1750024
- PAR ID:
- 10232060
- Date Published:
- Journal Name:
- USENIX Security Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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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.more » « less
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Distributed denial of service (DDoS) attacks have been prevalent on the Internet for decades. Albeit various defenses, they keep growing in size, frequency, and duration. The new network paradigm, Software-defined networking (SDN), is also vulnerable to DDoS attacks. SDN uses logically centralized control, bringing the advantages in maintaining a global network view and simplifying programmability. When attacks happen, the control path between the switches and their associated controllers may become congested due to their limited capacity. However, the data plane visibility of SDN provides new opportunities to defend against DDoS attacks in the cloud computing environment. To this end, we conduct measurements to evaluate the throughput of the software control agents on some of the hardware switches when they are under attacks. Then, we design a new mechanism, called
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