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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Distributed Security Network Functions against Botnet Attacks in Software-defined Networks
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.
more »
« less
- Award ID(s):
- 1723804
- PAR ID:
- 10095690
- Date Published:
- Journal Name:
- IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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