BGP update profiles and the implications for secure BGP update validation processing Geoff Huston PAM2007 5 April 2007.

Slides:



Advertisements
Similar presentations
BGP01 An Examination of the Internets BGP Table Behaviour in 2001 Geoff Huston Telstra.
Advertisements

Update Damping in BGP Geoff Huston Chief Scientist, APNIC.
Comparing IPv4 and IPv6 from the perspective of BGP dynamic activity Geoff Huston APNIC February 2012.
Part IV: BGP Routing Instability. March 8, BGP routing updates  Route updates at prefix level  No activity in “steady state”  Routing messages.
1 Experimental Study of Internet Stability and Wide-Area Backbone Failure Craig Labovitz, Abha Ahuja Merit Network, Inc Presented by Changchun Zou.
2006 – (Almost another) BGP Year in Review A BRIEF update to the 2005 report 18 October 2006 IAB Routing Workshop Geoff Huston APNIC.
1 Interdomain Routing Protocols. 2 Autonomous Systems An autonomous system (AS) is a region of the Internet that is administered by a single entity and.
BGP update profiles and the implications for secure BGP update validation processing Geoff Huston Swinburne University of Technology PAM April 2007.
BGP in 2009 Geoff Huston APNIC May Conventional BGP Wisdom IAB Workshop on Inter-Domain routing in October 2006 – RFC 4984: “routing scalability.
An Operational Perspective on BGP Security Geoff Huston GROW WG IETF 63 August 2005.
Internet Routing Instability Labovitz et al. Sigcomm 1997 Largely adopted from Ion Stoica’s slide at UCB.
Allocations vs Announcements A comparison of RIR IPv4 Allocation Records with Global Routing Announcements Geoff Huston May 2004 (Activity supported by.
Confidence Interval A confidence interval (or interval estimate) is a range (or an interval) of values used to estimate the true value of a population.
© Janice Regan, CMPT 128, CMPT 371 Data Communications and Networking BGP, Flooding, Multicast routing.
Analyzing Reliability and Validity in Outcomes Assessment (Part 1) Robert W. Lingard and Deborah K. van Alphen California State University, Northridge.
Interconnectivity Density Compare number of AS’s to average AS path length A uniform density model would predict an increasing AS Path length (“Radius”)
Measuring IPv6 Deployment Geoff Huston George Michaelson
Measuring IPv6 Deployment Geoff Huston George Michaelson
CAIA Seminar – 18 August 2007 – Taming BGP An incremental approach to improving the dynamic properties of BGP Geoff Huston.
BGP in 2011 Geoff Huston APNIC. Conventional (Historical) BGP Wisdom IAB Workshop on Inter-Domain routing in October 2006 – RFC 4984: “routing scalability.
Inter-Domain Routing Trends Geoff Huston APNIC March 2007.
1 Auto-Detecting Hijacked Prefixes? Routing SIG 7 Sep 2005 APNIC20, Hanoi, Vietnam Geoff Huston.
1 Border Gateway Protocol (BGP) and BGP Security Jeff Gribschaw Sai Thwin ECE 4112 Final Project April 28, 2005.
1 IP Routing table compaction and sampling schemes to enhance TCAM cache performance Author: Ruirui Guo, Jose G. Delgado-Frias Publisher: Journal of Systems.
Border Gateway Protocol. Intra-AS v.s. Inter-AS Intra-AS Inter-AS.
Performance Comparison of Ad Hoc Network Routing Protocols Presented by Venkata Suresh Tamminiedi Computer Science Department Georgia State University.
BGP Validation Russ White Rule11.us.
CS 3700 Networks and Distributed Systems
Types of risk Market risk
Step 1: Specify a null hypothesis
Geoff Huston APNIC March 2017
CS 3700 Networks and Distributed Systems
Auto-Detecting Hijacked Prefixes?
Auto-Detecting Hijacked Prefixes?
More Specific Announcements in BGP
Routing 2015 Scaling BGP Geoff Huston APNIC May 2016.
Text Based Information Retrieval
Goals of soBGP Verify the origin of advertisements
Measuring BGP Geoff Huston.
Analyzing Redistribution Matrix with Wavelet
BGPSEC Potential Optimizations for AS-PATH Prepending and Transparent Route Servers. sidr wg / Québec City Doug Montgomery
Auburn University COMP8330/7330/7336 Advanced Parallel and Distributed Computing Communication Costs (cont.) Dr. Xiao.
Elementary Statistics
Estimating with PROBE II
© 2002, Cisco Systems, Inc. All rights reserved.
\course\cpeg324-08F\Topic7c
Types of risk Market risk
Elementary Statistics
Outlier Discovery/Anomaly Detection
Routing and Addressing in 2017
Analyzing Reliability and Validity in Outcomes Assessment Part 1
TEAP XXV/8 Task Force Report
More Specific Announcements in BGP
Geoff Huston APNIC 7th caida/wide measurement workshop Nov
APNIC Trial of Certification of IP Addresses and ASes
Routing Table Status Report
Data Mining Anomaly/Outlier Detection
CS 430: Information Discovery
Geoff Huston September 2002
Routing Table Status Report
IPv4 Address Lifetime Expectancy Revisited
COS 461: Computer Networks
2005 – A BGP Year in Review February 2006 Geoff Huston
Routing Table Status Report
Volume 12, Issue 9, Pages (April 2002)
Data Mining Anomaly Detection
Data Communication: Routing algorithms
BGP: 2008 Geoff Huston APNIC.
Geoff Huston APNIC 7th caida/wide measurement workshop Nov
Data Mining Anomaly Detection
Presentation transcript:

BGP update profiles and the implications for secure BGP update validation processing Geoff Huston PAM2007 5 April 2007

Why? Secure BGP proposals all rely on some form of validation of BGP update messages Validation typically involves cryptographic validation, and may refer to further validation via a number resource PKI This validation may take considerable resources to complete. This implies that the overheads securing BGP updates in terms of validity of payload may contribute to: Slower BGP processing Slower propagation of BGP updates Slower BGP convergence following withdrawal Greater route instability Potential implications in the stability of the forwarding plane One of the approaches to securing BGP is to attach some form of credential to a BGP update message. These credentials would normally be originally placed into the message by the originator of the BGP message, and allow remote parties who receive the update message to be able to validate that the subject of the update message, namely the originating AS actually generated the original update message, and that the originating AS has the authority to speak about the address prefix. Validation of cryptographic information may entail significant processing overheads, and the concern is that measures to secure BGP may have a serious negative impact on the performance of BGP.

What is the question here? Validation information has some time span Validation outcomes can be assumed to be valid for a period of hours Should BGP-related validation outcomes be locally cached? What size and cache lifetime would yield high hit rates for BGP update validation processing? One way to mitigate this processing overhead is to observe that validation outcomes may be considered “valid” for a period of some hours. The question is then one of: If validation outcomes were to be caches what cache parameters (cache time and retention time) would be effective in mitigating the BGP validation workload?

Method Use a BGP update log from a single eBGP peering session with AS 4637 over a 14 day period 10 September 2006 – 23 September 2006 Examine time and space distributions of BGP Updates that have similar properties in terms of validation tasks

Update Statistics for the session Day Prefix Updates Duplicates: Prefix + Origin AS Duplicates AS Path Prefix + Comp-Path 1 72,934 60,105 (82%) 54,924 (75%) 34,822 (48%) 35,312 (48%) 2 79,361 71,714 (90%) 67,942 (86%) 49,290 (62%) 50,974 (64%) 3 104,764 93,708 (89%) 87,835 (84%) 65,510 (63%) 66,789 (64%) 4 107,576 94,127 (87%) 87,275 (81%) 64,335 (60%) 66,487 (62%) 5 139,483 110,994 (80%) 99,171 (71%) 68,096 (49%) 69,886 (50%) 6 100,444 92,944 (92%) 88,765 (88%) 70,759 (70%) 72,108 (72%) 7 75,519 71,935 (95%) 69,383 (92%) 56,743 (75%) 58,212 (77%) 8 64,010 60,642 (95%) 57,767 (90%) 49,151 (77%) 49,807 (78%) 9 94,944 89,777 (95%) 86,517 (91%) 71,118 (75%) 72,087 (76%) 10 81,576 78,245 (96%) 75,529 (93%) 63,607 (78%) 64,696 (79%) 11 95,062 91,144 (96%) 87,486 (92%) 72,678 (76%) 74,226 (78%) 12 108,987 103,463 (95%) 99,662 (91%) 80,720 (74%) 82,290 (76%) 13 91,732 87,998 (96%) 85,030 (93%) 72,660 (79%) 74,116 (81%) 14 78,407 76,174 (97%) 74,035 (94%) 64,994 (83%) 65,509 (84%) These are the raw figures from the 14 day capture period The first column is the number of prefixes that were updated by BGP (including duplicates) The second column is the percentage of these prefixes that were updated at least twice (i.e. were duplicated during the day) The third is the number of prefix plus origin AS pairs that were duplicated in the day. The fourth column uses prefix plus the AS path The fifth column uses a compressed AS path (i.e. removing repeated AS’s from the AS Path)

CDF by Prefix and Originating AS The CDF on the left indicates that the busiest 1% of all prefixes were responsible for 20% of the total number of BGP prefix updates over the 14 day period, and the top 10% of prefixes were responsible for 55% of all prefix updates The CDF on the right indicates that the busiest 1% of all origin AS’s occurred in 41% of all prefix updates, and the top 10% occurred in 80% of all prefix updates. These figures show a highly skewed distribution with strong heavy tail characteristics. They also indicate a strong likelihood of cacheability.

Time Distribution This figure indicates the time distribution of duplicated update information for the various ‘types’ of duplication, looking at prefixes, prefix plus origin, prefix plus path and prefix plus compressed path. The figures indicate that the overall majority of duplicates occur within one hour of the original update, and the recurrence rate drops off in the 30 – 40 hour range.

Time Spread This figure shows the same plot as the previous figure, but uses a relative percentage of all duplicates. The shaded area indicates that within 36 hours more than 80% of all duplicate types will have occurred.

Space Distribution Use a variable size cache simulator Assume 36 hour cache lifetime Want to know the hit rate of validation queries against cache size The next figures look at determining the most effective cache size for this data. The cache simulator is one that simulates all cache sizes simultaneously, using a 36 hour cache retention interval and a LRU cache replacement scheme

Prefix Similarity This is a simulation of a prefix cache, looking at the cache hot rates for each of the 14 days, using a 36 hour cache retention time and a LRU cache replacement algorithm. For 13 of the 14 days in the measurement period a cache size of 10,000 prefixes would have a cache hit rate of between 78% to 86%

Prefix + Origin Similarity This is similar to the previous figure, but in this case the cache key is “origination” which is the combination of the prefix and the origin AS. Here a cache of 10,000 entries would have an average daily hit rate of some 72%.

Prefix + Path Similarity This is the same as the previous figures, using this time a cache key of prefix plus AS path. Here a cache of 10,000 entries would have an average hit rate of some 38%.

Observations A large majority of BGP updates explore diverse paths for the same origination True origination instability occurs relatively infrequently (1:4) ? Validation workloads can be reduced by considering origination (prefix plus origin) and the path vector as separable validation tasks Further processing reduction can be achieved by treating a AS path vector as a sequence of AS paired adjacencies AS the text points out a significant proportion of BGP updates appear to be BGP performing a path exploration. The extent of origin instability, where a prefix is announced from multiple origin ASs is less common. The view of using the prefix plus AS path as the ‘unit’ of BGP security validation appears to represent a relatively suboptimal choice in terms of validation workload even with caching out validation outcomes. The workload can be reduced significantly by considering origination (prefix plus origin AS) and AS paths as distinct validation tasks. The next question is to what extent can path validation outcomes be cached, where the path and not the prefix is validated and the validation outcome cached.

AS Path Similarity This figure indicates that a cache of 10,000 AS paths with a retention period of 36 hours would have an average daily hit rate of some 77%

AS Pair Similarity AS path validation can be further decomposed into a task of a sequence of validation of AS pairs. This figure shows the AS pair validation cache hit rate for various cache sizes. A cache of 100 AS pairs would have an average hit rate of some 83%

Observations Validation caching appears to be a useful approach to addressing some of the potential overheads of validation of BGP updates Separating origination from path processing, using a 36 hour validation cache can achieve 80% validation hit rate using a cache of 10,000 Prefix + AS originations and a cache of 1,000 AS pairs Cacheing represents a very effective way to reduce the validation processing overhead associated with secure BGP measures. The data takes in this measurement period indicates that a cache of 10,000 originations can achieve an 80% validation hit rate, reducing the validation processing load by this amount. A cacne of 1,000 AS pairs appears capable of performing at a hit rate of 90% Part of the high AS Pair cache hit rate is due to the observation that in an AS path vector the AS pairs on the left are ‘closer’ to the BGP speaker, and near AS pair adjacencies will occur in almost every update, while AS pair adjacencies to the right of the AS path vector will be less frequent. The other factor here is the highly skewed distribution of Origin AS’s in updates where, as previously noted, 41% of all updates have 1% (or some 230) origin AS’s. This points to a more general observation about network stability and BGP that it appears that in general most of the network is extremely stable, and a very small subset of the network exhibits disproportionately high levels of routing instability.

What do we want from secure BGP? Validation that the received BGP Update has been processed by the ASs in the AS Path, in the same order as the AS Path, and reflects a valid prefix, valid origination and valid propagation along the AS Path? or Validation that the received Update reflects a valid prefix and valid origination, and that the AS Path represents a plausible sequence of validated AS peerings? By decomposing the BGP update validation task of validation of a prefix and an associated AS Path into separate origination validation and AS path validation, and then further decomposing AS Path validation into a sequence of AS pair validation tasks, and caching validation outcomes, the validation workload can be reduced considerably. The validation outcomes, however, are subtly different. The validation of the complete update, where each AS adds crypto information that covers what it sends as an update to its peer creates a cumulative outcome where a BGP update receiver can be assurred that not only is the prefix a valid one and the AS path in the update is a valid AS path in terms of the actual network topology, but it can also validate that the update itself has traversed the same sequence of AS’s as represented in the AS path. The validation outcome of origination and AS pairs can still provide assurance that the prefix is a valid one and that the prefix holder has authorized the origination of the prefix by the orign AS. However the AS pair validation can only validate that the AS path as represented in the update is a plausible path, and the update itself may not have been propagated along this AS path.

Thank You