We present BGP-iSec, an enhancement of the BGPsec protocol for securing BGP, the Internet’s inter-domain routing protocol. BGP-iSec ensures additional and stronger security properties, compared to BGPsec, without significant extra overhead.
The main improvements are:
(i) Security for partial adoption: BGP-iSec provides significant security benefits for early adopters,
in contrast to BGPsec, which requires universal adoption.
(ii) Defense against route leakage: BGP-iSec defends against route leakage, a common cause of misrouting that is not prevented by BGPsec.
(iii) Integrity of attributes: BGP-iSec ensures the integrity of integrity-protected attributes, thereby preventing announcement manipulation attacks not prevented by BGPsec.
We argue that BGP-iSec achieves these goals using extensive simulations as well as security analysis. The BGP-iSec design conforms, where possible, with the BGPsec design, modifying it only where necessary to improve security or ease deployment. By providing stronger security guarantees, especially for partial adoption, we hope BGP-iSec will be a step towards finally protecting interdomain routing, which remains, for many years, a vulnerability of the Internet’s infrastructure.
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Cutting Through the Noise to Infer Autonomous System Topology
The Border Gateway Protocol (BGP) is a distributed protocol that manages interdomain routing without requiring a centralized record of which autonomous systems (ASes) connect to which others. Many methods have been devised to infer the AS topology from publicly available BGP data, but none provide a general way to handle the fact that the data are notoriously incomplete and subject to error. This paper describes a method for reliably inferring AS-level connectivity in the presence of measurement error using Bayesian statistical inference acting on BGP routing tables from multiple vantage points. We employ a novel approach for counting AS adjacency observations in the AS-PATH attribute data from public route collectors, along with a Bayesian algorithm to generate a statistical estimate of the AS-level network. Our approach also gives us a way to evaluate the accuracy of existing reconstruction methods and to identify advantageous locations for new route collectors or vantage points.
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- Award ID(s):
- 2005899
- NSF-PAR ID:
- 10431501
- Date Published:
- Journal Name:
- Proceedings of IEEE INFOCOM 2022
- Page Range / eLocation ID:
- 1609 to 1618
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/INFOCOM48880.2022.9796874
