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Title: Enabling Dynamic Network Access Control with Anomaly-based IDS and SDN
In the Software Defined Networking (SDN) and Network Function Virtualization (NFV) era, it is critical to enable dynamic network access control. Traditionally, network access control policies are statically predefined as router entries or firewall rules. SDN enables more flexibility by re-actively installing flow rules into the switches to achieve dynamic network access control. However, SDN is limited in capturing network anomalies, which are usually important signs of security threats. In this paper, we propose to employ anomaly-based Intrusion Detection System (IDS) to capture network anomalies and generate SDN flow rules to enable dynamic network access control. We gain the knowledge of network anomalies from anomaly-based IDS by training an interpretable model to explain its outcome. Based on the explanation, we derive access control policies. We demonstrate the feasibility of our approach by explaining the outcome of an anomaly-based IDS built upon a Recurrent Neural Network (RNN) and generating SDN flow rules based on our explanation.
Authors:
; ;
Award ID(s):
1723663 2129164 2128607 2128107
Publication Date:
NSF-PAR ID:
10109941
Journal Name:
Proceedings of the 2019 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization
Page Range or eLocation-ID:
13 to 16
Sponsoring Org:
National Science Foundation
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