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Maverick: Detecting Network Configuration and Control Plane Bugs through Structural Outlierness

Authors:
Vasudevan Nagendra
Abhishek Pokala
Arani Bhattacharya
Samir Das

Keywords: Network, Control plane, Verification, Outliers, Machine Learning, Network Bugs, Anomaly Detection, Bug Severity.

Abstract:
Abstract—Proactive detection of network configuration bugs is important to ensure the proper functioning of networks and reducing the issues associated with network outages. In this research, we propose to build a control plane verification tool MAVERICK that detects the bugs in the network device configurations by effectively leveraging structural deviation i.e., outliers in the network configurations. MAVERICK automatically infers signatures from control plane configurations (e.g., Access Control Lists (ACL), route-maps, route-policies, and so on) and allows administrators to automatically detect bugs in the network configurations with minimal human intervention. The outliers calculated using signature-based outlier detection mechanism are further characterized for its severity and ranked or re-prioritized according to their criticality. We consider a wide set of heuristics and domain expertise factors for effectively reducing the false positives. Our evaluation on four medium to large-scale enterprise networks shows that MAVERICK can automatically detect the bugs present in the network with ≈86.4% accuracy. Furthermore, with minimal administrator inputs i.e., with a few minutes of signature re-tuning, MAVERICK allows the administrators to effectively detect ≈92 – 100% of the bugs present in the network, thereby ranking down less severe bugs and removing false positives.

Pages: 53 to 58

Copyright: Copyright (c) IARIA, 2021

Publication date: November 14, 2021

Published in: conference

ISSN: 2162-2116

ISBN: 978-1-61208-919-5

Location: Athens, Greece

Dates: from November 14, 2021 to November 18, 2021