CN111901201B - IPv6 network topology measurement target selection method - Google Patents

IPv6 network topology measurement target selection method Download PDF

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CN111901201B
CN111901201B CN202010754110.2A CN202010754110A CN111901201B CN 111901201 B CN111901201 B CN 111901201B CN 202010754110 A CN202010754110 A CN 202010754110A CN 111901201 B CN111901201 B CN 111901201B
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address
list
prefix
network topology
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CN111901201A (en
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郑儿
张宇
产毛宁
张尼
薛继东
苏马婧
孙彻
贾召鹏
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Harbin Institute of Technology
6th Research Institute of China Electronics Corp
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
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    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract

A method for selecting an IPv6 network topology measurement target relates to the field of IPv6 network topology measurement. The invention aims to improve the validity and the integrity of IPv6 network topology measurement. The method mainly comprises the following steps: the multi-source collects and fuses the IPv6 live address list; the fused IPv6 survival address list is used as the input of a prediction algorithm of the IPv6 survival address prefix list to obtain a predicted IPv6 survival address prefix list; and obtaining a final IPv6 network topology measurement target by utilizing a comprehensive target address generation method. The method can effectively improve the validity and the integrity of the IPv6 network topology measurement, namely, fewer targets are selected in the huge address space of IPv6, and the topology integrity can be obviously improved compared with the common uniform random sampling method. The method is used for selecting the IPv6 network topology measurement target.

Description

IPv6 network topology measurement target selection method
Technical Field
The invention relates to a method for selecting an IPv6 network topology measurement target, and relates to the field of IPv6 network topology measurement.
Background
At present, the deployment of the IPv6 has been leaped forward, and the understanding of the topological structure of the IPv6 is of great significance, but the IPv6 network topology measurement has three major challenges, firstly, the IPv6 address space is huge, and the whole address number is 2128Now allocated more than 2118Therefore, the whole network cannot be detected; secondly, the IPv6 address planning is complex, the address space division policy is various, and a universal target sampling method is difficult to find; finally, the actual use rate of the IPv6 address is low, and the efficiency of the general-survey topology measurement is low. The IPv6 network topology measurement target selection generally adopts a uniform random sampling method, but the granularity cannot be finely sampled in practice, and a large amount of invalid probes exist. On the other hand, the IPv6 address space is characterized by studying IPv6 list of surviving addresses (Hitlist) technique, i.e. collecting a set of surviving addresses that can approximately cover IPv6 networks, Gasser et al[1]The TUM Hitlist is proposed from the aspects of balance and unbiasedness, but the core target is IPv6 network host discovery instead of network topology discovery, and an IPv6 survival address list is not used for selecting an IPv6 network topology measurement target. While in the direction of IPv6 address generation, Foremski et al[2]An Encopy/IP system is provided, a machine learning technology of cluster analysis and Bayesian model is used for discovering an IPv6 address structure, and 6Gen is also used for similar work[3]Their essence is still IPv6 network host discovery and not topology discovery. Therefore, the problem of the prior art about target selection in the IPv6 network topology measurement is still not solved.
Disclosure of Invention
The invention aims to provide a method for selecting an IPv6 network topology measurement target based on an IPv6 survival address list to improve the validity and the integrity of IPv6 network topology measurement.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention discloses a method for selecting an IPv6 network topology measurement target, the general flow of the invention is shown in figure 1, and the implementation process of the method is as follows:
(1) the multi-source collects the IPv6 live address list and fuses:
the collected sources comprise more than two of DNS related databases (such as DNSDB and Rapid7), active measurement platform CAIDA Ark, comprehensive address acquisition items TUM Hitlists, open website interface Bitnode API and passive traffic acquisition (such as the traffic of CDN);
extracting IPv6 addresses from different sources respectively to form different address sets correspondingly, filtering according to the latest global unicast address allocation provided by IANA, and combining the address sets to obtain a fused IPv6 survival address list;
(2) the fused IPv6 survival address list is used as the input of a prediction algorithm of the IPv6 survival address prefix list to obtain a predicted IPv6 survival address prefix list;
the flow of the prediction algorithm of the IPv6 live address prefix list is as follows:
and predicting the IPv6 live address prefix list with fixed length L according to the IPv6 live address prefix list extracted from the IPv6 live address list HL contained in the R aiming at a target network under the specific IPv6 routable prefix R. Length L of RR∈[32,64]L ∈ {40, 48.., 64}, for x, which is a 16-bit binary number with 32 bitsjRepresented IPv6 address XiThen an HL-formatted representation containing n addresses:
HL＝{X1,X2,...,Xi,...,Xn}
Figure BDA00026109688700000216
xj＝{'0','1',...,'f'}
(2.1) multi-level density clustering: the layers are divided into 8-bit intervals according to different prefix lengths, 32-64. For LevelpFormalized representation:
Figure BDA0002610968870000021
p∈{40,48,...,L}
then, each LevelpPrefix value of
Figure BDA0002610968870000022
In turn as input for density clustering, here from XiBit 9 begins because the/32 prefix is fixed. Adopting DBSCAN density clustering algorithm, setting the clustering into K classes, and aiming at LevelpThe k-th class of l values are sorted into sets in ascending order
Figure BDA0002610968870000023
The mathematical formula can be expressed as:
Figure BDA0002610968870000024
Figure BDA0002610968870000025
will be provided with
Figure BDA0002610968870000026
Missing values between adjacent values as predictions
Figure BDA0002610968870000027
The greater p corresponds to
Figure BDA0002610968870000028
The higher the prediction priority, the formula is:
Figure BDA0002610968870000029
(2.2) heuristic suffix extension: for the prefix with the predicted length shorter than L, adopting a heuristic extension postfix method, and specifically adopting the corresponding clustering class postfix set in the step (2.1) for a certain predicted prefix
Figure BDA00026109688700000210
To expand, wherein
Figure BDA00026109688700000211
Corresponding t suffix sets containing t elements, and setting the intersection of the suffix sets
Figure BDA00026109688700000212
To predict high priority, the corresponding formalization represents:
Figure BDA00026109688700000213
Figure BDA00026109688700000214
Figure BDA00026109688700000215
Figure BDA0002610968870000031
combining the steps (2.1) and (2.2) to obtain LevelpPredicted IPv6 live prefix list HPLpFormalized as:
Figure BDA0002610968870000032
Figure BDA0002610968870000033
finally merging different HPLspThe output HPL is obtained, and the algorithm pseudo code is as follows:
Figure BDA0002610968870000034
(3) and obtaining a final IPv6 network topology measurement target by utilizing a comprehensive target address generation method.
Further, 2 steps are divided to select the target of IPv6 network topology measurement: (1) uniformly and randomly extracting the survival address list according to a prefix of/64; (2) and (3) obtaining a prefix list of prediction/64 according to an IPv6 live address prefix list prediction algorithm, splicing random interface identifications for each prefix, and finally combining the output of the 2 steps. The comprehensive scheme of the IPv6 network topology measurement target selection based on the IPv6 live address list is shown in FIG. 2.
The invention has the following beneficial technical effects:
the invention provides a method for selecting an IPv6 network topology measurement target, which can effectively improve the effectiveness and the integrity of IPv6 network topology measurement, namely, a small number of targets are selected in an IPv6 huge address space, and the topology integrity can be obviously improved compared with a common uniform random sampling method. The invention at least comprises the following three main contributions or technical effects: (1) the IPv6 survival address list is used for guiding the selection of the IPv6 network topology measurement target, and the problem that the target selection is low in effectiveness by a network segment uniform random sampling method caused by the huge address space of IPv6 is solved; (2) an IPv6 live address prefix prediction algorithm is provided, which supplements the address space which is possibly increased in the future due to the possible missing in list acquisition and guesses; (3) and (3) providing a comprehensive scheme for selecting the IPv6 network topology measurement target, and combining (1) and (2) to provide a scheme for selecting the IPv6 network topology measurement target based on an IPv6 survival address list.
The technical points of the invention are as follows: from the view of the IPv6 survival address list, the IPv6 survival address list collection technology of multiple sources is explored to improve the integrity of the IPv6 survival address list, a prediction algorithm of the IPv6 survival address prefix list is provided and evaluated, and finally, a technical scheme generated by an IPv6 network topology measurement target is provided and the effectiveness of an experimental evaluation scheme is designed.
Drawings
Fig. 1 is a general flow chart of a method for selecting an IPv6 network topology measurement target, and fig. 2 is a comprehensive scheme diagram of selecting an IPv6 network topology measurement target;
fig. 3 is a diagram showing the change trend of the accuracy rate under the increase of the prediction set, fig. 4 shows the change of the number of nodes along with the increase of the number of detection targets, and fig. 5 shows the change of the number of links along with the increase of the number of detection targets.
The specific implementation mode is as follows:
the first embodiment is as follows:
(1) list and fusion of IPv6 live addresses collected by multiple sources:
acquiring an IPv6 live address list from 5 different address sources, including:
rapid7_ FDNS: the FDNS data is part of Project Sonar public data of Rapid7, and FDNS _ ANY data therein, including all replies to ANY query, is used to extract AAAA records therein to finally obtain IPv6 addresses.
CAIDA _ Ark: the IPv6 topology dataset is used as part of the CAIDA Ark platform measurement data to probe all declared/48 or shorter random addresses in IPv6 prefixes by using Paris Traceroute technology. The invention extracts all IPv6 addresses appearing in the path from the IPv6 topology data of CAIDA2 month.
Bitnodes: bitnodes evaluate the size of bitcoin networks by discovering all reachable nodes in the network, and the present invention extracts IPv6 addresses from the node names using the API provided by Bitnodes.
TUM _ Responsive: through research on an IPv6 live address list, the German TUM university provides IPv6Hitlist service, and the invention directly adopts a reply IPv6 address provided by the service.
TUM _ Seeds: the Hitlist service of TUM university in Germany is used as a collection item, different collection sources are adopted, and according to actual data in a warehouse, Alexa, CAIDA _ dnname, CT (certificate _ Transparency), Zonefields, Openipmap and Traceroute are included, wherein the first 4 are used for inquiring AAAA records to obtain IPv6 addresses according to extracted domain names; the Openipmap is an IPv6 address extracted from the RIPE ipmap project; traceroute is to re-extract all router IPv6 addresses from Traceroute addresses in all other sources. The invention integrates different source addresses in the warehouse as a collection source.
And finally, cleaning and merging the survival address lists of different sources to obtain a fused IPv6 address survival list of about 17M. Cleaning: since the collected addresses also include special-purpose global unicast addresses such as 6to4 addresses in address translation, the screening is performed according to the latest global unicast address allocation provided by IANA.
Statistics of IPv6 live address lists of different sources as shown in table 1, it can be observed that different sources contribute differences in the converged IPv6 live address list, where monopoly: the number of addresses provided by the source in the fused IPv6 live address list is compared with the ratio of the intersection of the source address and the fused IPv6 live address list to the fused IPv6 live address list, so that differences between different source ratios and the exclusive ratios are found, and the CAIDA _ Ark is even basically consistent, which indicates that the acquisition techniques behind the different sources are different and have independence.
Table 1 IPv6 list of surviving addresses statistics
Figure BDA0002610968870000051
(2) Using the fused IPv6 live address list as an input of a prediction algorithm of the IPv6 live address prefix list to obtain a predicted IPv6 live address list prefix:
to evaluate the effectiveness of the predictive algorithm, the IPv6 surviving address list is filtered to a/64 surviving prefix list for a particular/32 prefix network, and the correct rate of the inventive algorithm prefluxpage (pp) is calculated for a random 50% of the list as the training set and the remaining 50% as the test set and compared to the control/ip (eip) results. Prediction set: the output prediction prefix list with the same number as the test set, the accuracy: the intersection of the prediction set and the test set accounts for the proportion of the test set. The invention performs experiments on three data sets, H1, H2 and H3, and the experimental analysis results are shown in Table 2.
TABLE 2 comparison of PrefixPrediction and Encopy/IP results
Figure BDA0002610968870000061
Experimental results show that the prefix prediction algorithm PrefixPrediction has a higher correct rate than Encopy/IP, which is 1.4-5.5 times higher than that of Encopy, and the prefix prediction algorithm PrefixPrediction has small intersection and strong complementarity. Observing the change trend of the accuracy rate under the condition of the increase of the prediction set as shown in fig. 3, it can be found that as the prediction set increases, the accuracy rate of Prefix comparison is higher than that of Encopy/IP, and the accuracy rates of Prefix comparison and Encopy/IP both show a linear increasing trend, which indicates that the prediction capabilities of both algorithms have stability.
(3) And (3) obtaining a final IPv6 network topology measurement target by utilizing a comprehensive target address generation method:
in order to verify the effect of the IPv6 network topology measurement target selection method based on the IPv6 live address list, aiming at a target network with a specific/32 routable prefix, the method HS and the URS (uniform random sampling per/64) are respectively adopted to adopt an ICMP-paris detection method for a certain number of targets, and the node number and the link number of the result are compared. A certain amount: in the method of the invention, the survival address list is 2 times of the number of uniform random samples of/64, and the corresponding URS randomly extracts the number from the uniformly randomly sampled addresses. The number of nodes: the number of different IPv6 interface addresses found in the traceroute path. Number of links: the number of different IPv6 interface address connections found in the traceroute path. The invention performs experiments on three data sets T1(2001:16b8:: 32), T2(2a02:06b8:: 32) and T3(240e:00e0:: 32) by using different measuring points respectively, and the experimental results are shown in Table 3.
Experimental results show that the IPv6 network topology measurement target selection method HS based on the IPv6 survival address list obviously improves the integrity of topology discovery compared with a uniform random sampling method URS, the new topology discovery rate is over 94%, meanwhile, the results of the topology discovery are observed to change along with the increase of detection targets as shown in figures 4 and 5, and the HS is found to have better performance than the URS results. Integrity of the topology: the number of nodes discovered and the number of links. The new discovery rate of the topology is as follows: HS accounts for the ratio of HS compared to newly discovered nodes and links of URS.
TABLE 3 comparison of HS and URS test results
Figure BDA0002610968870000062
The references cited in the present invention are detailed below:
[1]Gasser O,Scheitle Q,Foremski P,et al.Clusters in the Expanse:Understanding and Unbiasing IPv6 Hitlists[C].Internet Measurement Conference,2018:364-378.
[2]Foremski P,Plonka D,Berger A W,et al.Entropy/IP:Uncovering Structure in IPv6Addresses[C].Internet Measurement Conference,2016:167-181.
[3]Murdock A,Li F,Bramsen P,et al.Target generation for internet-wide IPv6 scanning[C].Internet Measurement Conference,2017:242-253.

Claims (5)

1. a method for selecting an IPv6 network topology measurement target is characterized in that the method is realized by the following steps:
(1) the multi-source collects the IPv6 live address list and fuses:
the collected sources comprise more than two of a DNS related database, an active measurement platform CAIDAArk, a comprehensive address acquisition item TUM Hitlists, an open website interface Bitnode API and passive flow acquisition;
extracting IPv6 addresses from different sources respectively to form different address sets correspondingly, filtering according to the latest global unicast address allocation provided by IANA, and combining the address sets to obtain a fused IPv6 survival address list;
(2) the fused IPv6 survival address list is used as the input of a prediction algorithm of the IPv6 survival address prefix list to obtain a predicted IPv6 survival address prefix list;
the flow of the prediction algorithm of the IPv6 live address prefix list is as follows:
for a target network under a specific IPv6 routable prefix R, predicting an IPv6 live address prefix list with the fixed length of L according to an IPv6 live address prefix list contained in the R and extracted from an IPv6 live address list HL; length L of RR∈[32,64]L ∈ {40, 48.., 64}, for x, which is a 16-bit binary number with 32 bitsjRepresented IPv6 address XiThen contains n addressesHL formalized representation of:
HL＝{X1,X2,...,Xi,...,Xn}
Figure FDA0003195868340000011
xj＝{′0′,′1′,...,′f′}
the following introduces a list of IPv6 live address prefixes predicted from HL by two steps:
(2.1) multi-level density clustering: dividing the hierarchy into 8 bit intervals according to different prefix lengths of 32-64, and regarding the hierarchy LevelpFormalized representation:
Figure FDA0003195868340000012
p∈{40,48,...,L}
then, each LevelpPrefix value of
Figure FDA0003195868340000013
In turn as input for density clustering, here from XiBit 9 starts because the/32 prefix is fixed; adopting DBSCAN density clustering algorithm, setting the clustering into K classes, and aiming at LevelpThe k-th class of l values are sorted into sets in ascending order
Figure FDA0003195868340000014
The mathematical formula can be expressed as:
Figure FDA0003195868340000015
Figure FDA0003195868340000016
will be provided with
Figure FDA0003195868340000017
Missing values between adjacent values as predictions
Figure FDA0003195868340000018
The greater p corresponds to
Figure FDA0003195868340000019
The higher the prediction priority, the formula is:
Figure FDA00031958683400000110
(2.2) heuristic suffix extension: for the prefix with the predicted length shorter than L, adopting a heuristic extension postfix method, and specifically adopting the corresponding clustering class postfix set in the step (2.1) for a certain predicted prefix
Figure FDA0003195868340000021
To expand, wherein
Figure FDA0003195868340000022
Corresponding t suffix sets containing t elements, and setting the intersection of the suffix sets
Figure FDA0003195868340000023
To predict high priority, the corresponding formalization represents:
Figure FDA0003195868340000024
Figure FDA0003195868340000025
Figure FDA0003195868340000026
Figure FDA0003195868340000027
combining the steps (2.1) and (2.2) to obtain LevelpPredicted IPv6 live prefix list HPLpFormalized as:
Figure FDA0003195868340000028
Figure FDA0003195868340000029
finally merging different HPLspObtaining an output HPL;
(3) and obtaining a final IPv6 network topology measurement target by utilizing a comprehensive target address generation method.
2. The method for selecting the target for measuring the IPv6 network topology according to claim 1, wherein in the step (3), the step of selecting the target for measuring the IPv6 network topology is divided into 2 steps: (1) uniformly and randomly extracting the survival address list according to a prefix of/64; (2) and according to the IPv6 live address prefix list prediction algorithm, obtaining a predicted/64 prefix list, splicing random interface identifications for each prefix, and finally combining the output of the 2 steps to obtain a final IPv6 network topology measurement target.
3. The IPv6 network topology measurement target selection method according to claim 1 or 2, wherein in step (1), the DNS related databases can be DNSDB and Rapid 7.
4. The method for selecting the target for IPv6 network topology measurement according to claim 3, wherein in step (1), the passive traffic acquisition refers to traffic of the CDN.
5. The method for selecting the IPv6 network topology measurement target according to claim 3, wherein in step (1), the IPv6 live address list is obtained from five different address sources.
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