Internet autonomous domain level topology inference method based on global view

Abstract

The invention discloses an Internet autonomous domain-level topology inference method based on a global view, which comprises the following steps: firstly, constructing an Internet AS-level topology association graph, organizing elements related to Internet AS-level topology through the association graph, and then inferring an AS business relationship by using a Bayesian network, the method comprises the following steps of: firstly, deducing an AS hierarchical structure based on a series of heuristic methods, then correcting AS-level topology based on the constraint of global view with similar routing policies of AS in the same hierarchy of the Internet, and finally judging whether to continuously iteratively deduce the AS business relationship and the hierarchical structure according to a set threshold value. According to the method disclosed by the invention, the elements related to the AS-level topology are organized through the incidence relation graph, so that the complex incidence relation among the elements can be better mined, and meanwhile, the AS-level topology of the Internet can be more accurately deduced under the constraint that the AS in the same level of the Internet has a global view with a similar routing strategy.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet autonomous domains, and specifically relates to an Internet autonomous domain-level topology inference method based on a global perspective. Background Art

[0002] The Internet Autonomous Systems (AS) level topology is the coarsest-grained topology of the Internet. It is a hierarchical structure consisting of autonomous domains and business relationships between autonomous domains. The global Internet has more than 70,000 independent and autonomous ASs. AS is a collection of IP routing prefixes under the control of a single or multiple network operators. AS is identified by a unique 16-bit or 32-bit AS number. Each AS is managed using a unified routing policy, while different commercial routing policies are set between ASs through the Border Gateway Protocol (BGP) to achieve traffic exchange and global reachability.

[0003] According to the different commercial routing strategies between ASs, the commercial relationships between ASs are usually divided into three types: Provider-to-Customer (P2C), Peer-to-Peer (P2P) and Sibling-to-Sibling (S2S). In P2C, customers need to pay the provider for traffic forwarding. In P2P and S2S, traffic can be exchanged between ASs for free. The difference between P2P and S2S is that ASs belonging to S2S are usually in the same operator, so they will exchange all types of traffic, while the two parties in P2P will only exchange the routes required to reach their respective networks and the networks obtained from customers. When inferring AS commercial relationships, ASs belonging to the same operator are usually regarded as S2S, and only P2P and P2C are considered in further inferences.

[0004] The complex business relationship between ASs determines that the AS-level topology is a hierarchical structure, including Tier1, Tier2, Tier3, Tier4 and Tier5. Tier1 is the top-level supplier-free AS of the Internet. Tier2, Tier3 and Tier4 are national, regional and local ASs respectively, which purchase traffic forwarding services from the upper-level AS and provide access services to the lower-level AS. Tier5 is a stub AS and a multi-host AS that does not forward traffic. With the rise of content providers, the traffic exchange of the Internet is becoming more and more flat, but this does not affect the fact that the Internet AS-level topology is essentially a hierarchical structure. The Internet AS-level topology depicts the true commercial nature of Internet inter-domain routing and is widely used in further research on Internet security monitoring, service quality optimization, network congestion monitoring and other related research.

[0005] The Internet AS-level topology includes topology structure, AS business relationship and AS hierarchy. The topology information is composed of BGP routing paths collected from multiple BGP routing observation points (Vantage Point, VP). Due to the limitations of the location and number of VPs, the obtained topology is incomplete and biased. On the basis of obtaining the AS-level topology base map, the business relationship and hierarchy of the AS are inferred in combination with the registration information related to the AS, and the complete AS-level topology can be obtained. The existing AS-level topology inference method is based on the assumption of the valley-free property (that is, the customer will not announce the routing path obtained from other suppliers or peers to its supplier AS), and mainly focuses on AS business relationship inference. On this basis, the AS hierarchy is inferred through a series of heuristic methods.

[0006] The AS-Rank method proposed in the document "Luckie M, Huffaker B, Dhamdhere A, et al. AS relationships, customercones, and validation. Proceedings of IMC, New York, NY, USA 2013, 243-256" does not rely on the assumption of the existence of valley-free paths or the pursuit of maximization of quantity. Instead, it is based on the assumption of global interconnection of ASs and uses the Bron / kerbosch method proposed in the document "Bron C and Kerbosch J. Algorithm 457: Finding All Cliques of an Undirected Graph. CACM, 1973" to obtain tier-1 ASs, and then uses a heuristic method to infer AS business relationships. The paper "Jin Y, Scott C, Dhamdhere A, et al. Stable and Practical AS Relationship Inference with ProbLink. 16th USENIX Symposium on NSDI, Boston, MA, USA, 2019, 581-598" first introduced a probabilistic inference algorithm, using a Bayesian network framework to integrate many noisy but useful attributes, selecting the highest probability relationship for each link, and being insensitive to the position of VP and the order of inferred link relationships. The paper "Jin Z, Shi X, Yang Y, et al. Toposcope: Recover as relationships from fragmentary observations. Proceedings of IMC, USA, 2020, 266-280" uses a Bayesian network to explore the interdependence between various link features and the uneven distribution of features observed from a biased VP distribution. However, these methods are difficult to accurately mine the complex correlations between different elements related to the AS-level topology. They only consider the constraints of neighboring ASs and lack the constraints of the global vision of the global Internet. That is, ASs in the same level of the Internet have similar routing strategies, which leads to deviations in AS-level topology inference.

[0007] In summary, existing AS-level topology inference methods mainly focus on reasoning about AS business relationships, and on this basis infer the AS hierarchy. Such methods are limited by the location and number of VPs, and only use the information of neighboring ASs and lack a global perspective, without considering the global hierarchy of the AS-level topology, that is, ASs at the same level of the Internet have similar routing strategies. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides an Internet autonomous domain-level topology inference method based on a global perspective, which organizes the elements related to the AS-level topology through an association relationship diagram, and can better explore the complex association relationships between the elements. At the same time, under the constraint of the global perspective that ASs at the same level of the Internet have similar routing strategies, the Internet AS-level topology can be inferred more accurately.

[0009] The technical solution adopted by the present invention is: a method for inferring Internet autonomous domain-level topology based on a global perspective, and the specific steps are as follows:

[0010] S1. Construct an Internet AS-level topology association diagram;

[0011] S2, based on step S1, using Bayesian network to infer AS business relationship;

[0012] S3, based on steps S1 and S2, performing AS hierarchy division;

[0013] S4, after inferring the AS business relationship and hierarchical structure based on steps S2-S3, perform AS-level topology correction based on the meta-path, that is, add a global view constraint to adjust the AS business relationship and hierarchical structure;

[0014] S5. Based on step S4, determine the number of AS business relationships and hierarchical structure adjustments. If it is greater than the set threshold, repeat steps S1-S4 with the adjusted hierarchy as input, and continue to infer AS business relationships and AS hierarchical structures until it is less than the set threshold. Stop iteration, output the results, and complete the Internet autonomous domain-level topology inference.

[0015] Furthermore, the step S1 is specifically as follows:

[0016] The nodes in the association relationship graph include: AS, AS link, network operator, IXP, traffic exchange infrastructure and VP.

[0017] The attributes in the association relationship diagram include: AS attributes and AS link attributes.

[0018] Among them, the attributes of AS include AS type and AS hierarchy; the attributes of AS link include: AS business relationship, distance between AS link and VP, number of VPs observed to AS link, and BGP community value.

[0019] The nodes of the association relationship graph are associated with each other through different relationships, including: a network operator operates an AS, a network operator operates an IXP, an AS exchanges traffic at an IXP, an AS exchanges traffic at a traffic exchange infrastructure, an IXP is located at a traffic exchange infrastructure, a VP belongs to an AS, an AS link has an active / destination AS, and a VP observes an AS link.

[0020] The data of the association relationship graph comes from a public data set, including: BGP routing path and routing registration information.

[0021] The route registration information includes: operator information, AS type information, IXP information and BGP community value meaning information.

[0022] The data of the association relationship diagram are as follows:

[0023] (1) BGP routing path;

[0024] All BGP routing paths within five days from the public database of Route-Views and RIPE RIS are downloaded and irrelevant and erroneous paths are filtered out. The BGP routing paths after filtering are the BGP routing paths of the association relationship graph.

[0025] The filtering process is as follows:

[0026] First, all AS paths with destinations as IPv4 prefixes are extracted. Then, all paths that have appeared within five days are retained to try to retain backup paths that appear briefly. Then, paths with reserved AS numbers and AS loop paths caused by routing poisoning are filtered out.

[0027] The BGP routing path information includes: AS, AS link, VP, the distance between the AS link and the VP, the number of VPs observed to the AS link, and the BGP community value of the AS link.

[0028] (2) Operator information;

[0029] The operator information data includes the mapping between AS and operator, which comes from CAIDA. Each AS has a corresponding operating organization. ASs belonging to the same operating organization are regarded as S2S relationships, and the S2S relationship is not inferred in subsequent inferences.

[0030] (3) AS type information;

[0031] Download and extract the business type data of AS in PeeringDB, and merge the business types of ASs with similar businesses and services, that is, "Cable / Digital Subscriber Line / Internet Service Provider", "Network Service Provider" and "Network Service" are merged into "Transmission / Access Provider"; "Government Network", "Education / Research" and "Enterprise Network" are merged into "Enterprise Network"; "Route Collector", "Route Server" and "Non-profit Network" are merged into "Non-profit Network", and "Content Provider" remains "Content Provider".

[0032] Then, the "non-profit network" in the merged AS business type data is ignored, and the AS type information of the association relationship diagram is obtained, including: "transmission / access provider", "content provider", and "enterprise network".

[0033] Among them, "transmission / access providers" provide traffic exchange and access services on the Internet, "content providers" are suppliers of network content, and "enterprise networks" are mainly customers, and on the Internet they are mainly ASs that use Internet services.

[0034] (4) IXP information;

[0035] IXP is a third-party organization that interconnects different ASes and exchanges data. The IXP information data used comes from data aggregated by CAIDA from PeeringDB, Hurricane Electric, Packet Clearing House, Wikipedia, BGP Looking Glass, and GeoNames.

[0036] The IXP information data includes: the name of the IXP, the traffic exchange infrastructure where it is located, the IPv4 prefix, the network operator and the member AS.

[0037] IXP uses bilateral and multilateral peering. Multilateral peering uses the routing server provided by the IXP as an intermediary to achieve dense peering. The routing server belongs to the AS of the IXP and is filtered out from the AS path.

[0038] (5) The meaning of BGP community value;

[0039] Download data from the 18 routing registries listed in the IRR and extract BGP community values ​​and their meanings. The BGP community value is an optional 32-bit number for BGP messages. The first 16 bits are the AS of the community value, and the last 16 bits represent the meaning of the community value, which is used to encode additional information related to the AS path. The AS business relationship inferred from the meaning of the BGP community value is used as real data to verify the inference results. The information of the network operation website is extracted from PeeringDB, and the import and export rules of the AS in the data of the RIPE NCC database are used as supplements to the meaning of the BGP community value.

[0040] Among them, for other BGP community values ​​whose meanings cannot be found, they are added to the association relationship graph.

[0041] Furthermore, the step S2 is specifically as follows:

[0042] S21, determine the information required for inferring AS business relationships, including: neighbor information, observation information, hierarchy information, and coexistence information;

[0043] (1) Neighbor information;

[0044] Neighbor information includes: AS link triplet. AS link triplet is three adjacent links on the same path.

[0045] All BGP routing paths are split into triplets. For the first hop of the path, add a NULL as the first tuple, and for the last hop of the path, add NULL as the last tuple. The ratio of each business relationship between the previous hop and the next hop of the intermediate AS is used as the probability, and the probability is divided into high, medium and low, with values ​​greater than 0.1, 0.01 to 0.1, and less than 0.01.

[0046] (2) Observation information;

[0047] The observation information includes: the number of VPs that observe the AS link, the distance to the VP, and non-path information.

[0048] Among them, the number of VPs observing the AS link greater than or equal to 10 is considered to be observed by a large number of VPs, and the number less than 10 is considered to be observed by a small number of VPs; the distance to the VP less than or equal to 2 is considered to be close to the VP, and the distance greater than 2 is considered to be far away from the VP; non-path information refers to neighbors in the AS topology structure but not appearing in the same path at the same time, which is used to describe the probability of P2P or P2C neighbors.

[0049] (3) Hierarchical information;

[0050] The hierarchical information includes: the location of the AS and the location of the VP.

[0051] Among them, ASs at the same level have similar business strategies, and AS level information can provide constraints from a global perspective.

[0052] (4) information about getting along;

[0053] The information about the same network includes: being in the same IXP and being in the same traffic exchange infrastructure.

[0054] S22, construct a training set for the Bayesian network;

[0055] First, the data is grouped according to VP, that is, the BGP routing path is divided into N groups according to VP.

[0056] Among them, the data of observation points belonging to "full VP" are divided into N-1 groups, and the remaining data belonging to "partial VP" are divided into one group. When grouping data, the level of VP is considered, and VPs of different levels are evenly divided into each group.

[0057] Then, the AS-Rank method is used to infer the preliminary AS business relationship for each group. Then, the preliminary AS business relationship data of each group are voted by ensemble learning to obtain high-confidence AS business relationships as the training set of the Bayesian network.

[0058] Among them, the preliminary AS business relationship results of each group are integrated and voted to obtain trusted links and fuzzy links, and the inferences that are consistent with at least 80% of the groups are regarded as consensus links. When the consensus link is P2P, if no more than 20% of the groups observe the link, it is regarded as a trusted link; when the consensus link is P2C, if at least 80% of the groups observe the link, it is regarded as a trusted link; the remaining links except the trusted links are regarded as fuzzy links.

[0059] S23, based on step S22, the Bayesian network structure is obtained, the EM algorithm is used to train the Bayesian network, and finally the AS business relationship is inferred;

[0060] Taking the high-confidence AS business relationship as input, the maximum-minimum hill climbing (MMLC) structural learning algorithm is used to obtain the probability distribution between each element. After removing the edges with smaller probabilities, the Bayesian network structure associated with each element is obtained. Then, taking the AS business relationship of the high-confidence trusted link and the Bayesian network structure as input, the EM algorithm is used to obtain the parameters of the Bayesian network. Finally, the trained Bayesian network is used to infer the business relationship between ASs with low confidence obtained by ensemble learning voting.

[0061] The high-confidence AS business relationship is a credible link, and the low-confidence AS business relationship is a fuzzy link. In subsequent iterations, only the EM algorithm needs to be used to update the conditional probability and infer the low-confidence AS business relationship.

[0062] Furthermore, the step S3 is specifically as follows:

[0063] Based on steps S1 and S2, the Internet AS-level topology is divided into five levels: Tier 1, Tier 2, Tier 3, Tier 4, and Tier 5. The inferred order is Tier 5, Tier 4, Tier 1, Tier 2, and Tier 3, as follows:

[0064] (1) Tier 5 is the lowest level AS, which has no customers and does not forward traffic. It includes stub AS and multi-homed AS.

[0065] Among them, a stub AS refers to an AS that is only connected to one AS and has no customers, and a multi-homed AS refers to an AS that is connected to multiple ASs but does not forward traffic.

[0066] (2) Tier 4 is a small local supplier with only a small number of customer ASs;

[0067] Among them, after Tier5 is deleted in the topology, ASs without customers are iteratively searched and deleted, and the deleted AS is Tier4.

[0068] (3) Tier 1 is the top-level densely peered AS without any upstream providers;

[0069] First, use the Bron / kerbosch algorithm to find all the clusters, then select the largest cluster with the largest transmission degree and no more than 10 ASs, then select other ASs that meet the conditions and add them to the largest cluster, and finally use the Bron / kerbosch algorithm again to find the largest cluster by the sum of the transmission degrees. This largest cluster is Tier 1.

[0070] (4) Tier 2 refers to some large regional and national Internet providers;

[0071] Tier 2 ASs are not inclined to be peers with other Tier 1 ASs. Taking Tier 1 as the initial set, sort the ASs according to the transmission degree and find the smallest set that satisfies weak partitioning. Tier 2 is the AS in the set except Tier 1.

[0072] (5) Tier 3 is a regional access provider that connects with a small number of Tier 1 and Tier 2 to provide network services to the lower-level ASs;

[0073] Tier 3 is the remaining AS after inferring other levels of AS.

[0074] Furthermore, in step S2, when the AS business relationship is first inferred, the AS hierarchy is roughly divided based on the AS topology and node degree method, as follows:

[0075] First, the AS with degree 1 in the topology is considered as Tier 5, and the AS with degree 2 connected to Tier 5 is considered as Tier 4. Then, the Bron / Kerbosch algorithm is used to find the largest group as Tier 1. Then, the AS connected to Tier 1 with a degree greater than or equal to 100 is considered as Tier 2. Finally, the remaining AS is Tier 3. In subsequent iterations, the AS hierarchy information inferred in step S3 is added.

[0076] Furthermore, the step S4 is specifically as follows:

[0077] The constraint of the global vision is that the roles of ASs at the same level in Internet traffic exchange are similar, and their business strategies are similar. The meta-path of the association relationship graph is used to measure this similarity.

[0078] Among them, the meta-path is all the paths composed of nodes and relationships between two nodes in the association graph. The meta-path in the association graph corresponds to the features in the existing dataset. The semantics under different paths means different similarities. The meta-path between AS links is used to measure the similarity of AS links at the same level and between different levels.

[0079] Define the AS-level topological association graph G=(V,E), the node type mapping function is φ:V→A, and the edge type mapping function is ψ:E→R.

[0080] Among them, V represents a node, E represents an edge, each v∈V belongs to a special node type φ(v)∈A, and each link e∈E belongs to a special relationship type ψ(e)∈R.

[0081] Using network mode T G =(A,R) to describe the meta-level of the AS-level topological association graph, that is, a directed graph defined on node type A, with edges being relationships from R.

[0082] The network mode includes: AS link, AS, type of AS, IXP where AS is located, traffic exchange infrastructure where AS is located, network operator where AS is located, AS community value marking AS link in BGP path, distance of AS link from observation point (long distance if greater than 2 hops, otherwise short distance), number of VPs observed in AS link (large number of VPs if greater than or equal to 10, otherwise small number of VPs).

[0083] The meta-paths related to the AS business relationship in the network model include: (AS link, AS, IXP, AS, AS link), (AS link, AS, traffic exchange infrastructure, AS, AS link), (AS link, AS, AS type, AS, AS link), (AS link, AS, network operator, AS, AS link), (AS link, BGP community value, AS link), (AS link, long distance, AS link), (AS link, short distance, AS link), (AS link, a large number of VPs, AS link), (AS link, a small number of VPs, AS link).

[0084] In the network mode T G =(A,R), the meta-path P in the AS-level topological association graph is represented as Then a node A1 and node A l The compound relationship between

[0085] Among them, A l Indicates the lth node type, R l-1 Represents the l-1th relationship type.

[0086] For a given symmetric meta-path P, the Pathsim metric expression for AS links x and y of the same type is defined as follows:

[0087]

[0088] Among them, p x→y represents the path between x and y, p x→x represents the path between x and x, p y→y represents the path between y and y.

[0089] Based on the Internet AS-level topology association network G = (V, E) and network model T G , meta-path P = (A1A2…A l ), calculate the matrix

[0090] in, Indicates A i and A j The adjacency matrix of ij Represents the value of the i-th row and j-th column of M, that is, the object x under the meta-path P i ∈A1 and x j ∈A l The number of paths between .

[0091] Then from the same type of x i and x j The Pathsim between two objects is

[0092] Among them, M ii and M jj Indicates that given a meta-path in the network x i and x j visibility.

[0093] Then traverse all the levels, for those in (Tier i ,Tier j ) of the link L(i,j)(Tier i The level is not lower than Tier j The Pathsim between P2P links and P2C links is calculated respectively.

[0094] When obtaining the calculation matrix M, the common number of IXPs, BGP community values, and network operators connected to the AS links is calculated, and then normalized to characterize the three types of meta-paths: IXP, BGP community value, and network operator.

[0095] Then, for a given business relationship c1∈{P2P,P2C}, the average similarity ranking between each AS link and the rest of the AS links is calculated, and the AS links L ranked before Δ are considered similar. Similar (i, j), and the remaining AS links with lower rankings are regarded as dissimilar links L Dissimilar (i,j).

[0096] Among them, Δ is set according to the actual situation.

[0097] Then for the dissimilar AS link L Dissimilar (i,j) further judges that the dissimilar AS links L Dissimilar (i,j) and in (Tier i ,Tier j ) calculates the similar links of another business relationship c2∈{P2P,P2C},c1≠c2, and considers the top Δ links as being able to be adjusted, that is, adjusting them to another business relationship c2. Dissimilar (i,j) The remaining unadjusted links, if Tier i If it is not Tier 1, then i-1 ,Tier j ) is calculated based on similar links. If Tier j If not Tier 5, then (Tier i ,Tier j+1 ) are calculated, and the links ranked higher than Δ are considered as adjustable links L CandidateFinally, the AS links that have not been adjusted are no longer adjusted.

[0098] After traversing all levels, determine L Candidate Whether the AS level in the AS needs to be adjusted. For any AS, calculate the number of times that all its associated links need to modify the level. If the number of times that the level needs to be modified is greater than the number that does not need to be modified, then modify the level of the AS.

[0099] Beneficial effects of the present invention: The method of the present invention first constructs an Internet AS-level topology association relationship graph, organizes the elements related to the Internet AS-level topology through the association relationship graph, then uses the Bayesian network to infer the AS business relationship, then infers the AS hierarchy based on a series of heuristic methods, and then corrects the AS-level topology based on the constraint of the global vision that ASs at the same level of the Internet have similar routing strategies, and finally determines whether it is necessary to continue iteratively inferring the AS business relationship and hierarchy according to the set threshold. The method of the present invention overcomes the deviation caused by the position and number of observation points in the existing AS-level topology inference method, organizes the elements related to the AS-level topology through the association relationship graph, and can better explore the complex association relationship between the various elements. At the same time, under the constraint of the global vision that ASs at the same level of the Internet have similar routing strategies, it can narrow the solution space and achieve the AS-level topology inference accuracy that is difficult to achieve by relying solely on local observations and neighbor information, effectively reducing the deviation caused by the position and number of observation points, and can more accurately infer the Internet AS-level topology. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 This is a flow chart of a method for Internet autonomous domain-level topology inference based on a global perspective of the present invention.

[0101] Figure 2 It is a schematic diagram of the Internet AS-level topology association relationship diagram in an embodiment of the present invention.

[0102] Figure 3 Schematic diagram of the Bayesian network structure in an embodiment of the present invention.

[0103] Figure 4 It is a network model schematic diagram of the Internet AS-level topology association relationship diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0104] The method of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0105] like Figure 1 As shown, a flowchart of an Internet autonomous domain-level topology inference method based on a global perspective of the present invention is shown, and the specific steps are as follows:

[0106] S1. Construct an Internet AS-level topology association diagram;

[0107] S2, based on step S1, using Bayesian network to infer AS business relationship;

[0108] S3, based on steps S1 and S2, performing AS hierarchy division;

[0109] S4, after inferring the AS business relationship and hierarchical structure based on steps S2-S3, perform AS-level topology correction based on the meta-path, that is, add a global view constraint to adjust the AS business relationship and hierarchical structure;

[0110] S5. Based on step S4, determine the number of AS business relationships and hierarchical structure adjustments. If it is greater than the set threshold, repeat steps S1-S4 with the adjusted hierarchy as input, and continue to infer AS business relationships and AS hierarchical structures until it is less than the set threshold. Stop iteration, output the results, and complete the Internet autonomous domain-level topology inference.

[0111] In this embodiment, the step S1 is specifically as follows:

[0112] The Internet AS-level topology relationship diagram is as follows: Figure 2 As shown, the nodes in the association relationship diagram include: AS, AS link, network operator, IXP, traffic exchange infrastructure and VP.

[0113] The attributes in the association relationship diagram include: AS attributes and AS link attributes.

[0114] Among them, the attributes of AS include AS type and AS hierarchy; the attributes of AS link include: AS business relationship, distance between AS link and VP, number of VPs observed to AS link, and BGP community value.

[0115] One of the goals of this embodiment is to infer the business relationship of AS. In essence, it is to classify AS links, and AS links are also regarded as nodes. Using AS links as nodes in the association relationship graph can better characterize the characteristics of AS links. The nodes of the association relationship graph are associated with each other through different relationships. The specific association relationships are as follows: Figure 2 As shown, by using the association relationship graph, the complex and diverse elements related to the AS-level topology can be associated in the form of a graph, which can better explore the complex association relationships between multiple elements and better constrain them from a global perspective, thereby more accurately inferring the business relationship and hierarchical structure of the AS.

[0116] The nodes of the association relationship graph are associated with each other through different relationships, including: a network operator operates an AS, a network operator operates an IXP, an AS exchanges traffic at an IXP, an AS exchanges traffic at a traffic exchange infrastructure, an IXP is located at a traffic exchange infrastructure, a VP belongs to an AS, an AS link has an active / destination AS, and a VP observes an AS link.

[0117] The data of the association relationship graph comes from a public data set, including: BGP routing path and routing registration information.

[0118] The routing registration information includes: operator information, AS type information, IXP (Internet Exchange Point) information and BGP community value meaning information.

[0119] The data of the association relationship diagram are as follows:

[0120] (1) BGP routing path;

[0121] BGP routing paths are the most important data in the Internet AS-level topology association diagram, providing AS interconnection information and observation information. Route-Views and RIPE RIS's routing collectors are deployed in different locations on the Internet and peer with BGP routers from multiple ASs to collect BGP path information from all over the world. They regularly save snapshots of their status. Different VPs collect AS-level views of the Internet from different angles, and aggregating them together yields the global Internet AS-level topology. Of course, due to the number and location limitations of VPs and the existence of backup links, such a topology is not complete.

[0122] All BGP routing paths within five days from the public database of Route-Views and RIPE RIS are downloaded and irrelevant and erroneous paths are filtered out. The BGP routing paths after filtering are the BGP routing paths of the association relationship graph.

[0123] The filtering process is as follows:

[0124] First, extract all AS paths with destinations as IPv4 prefixes. Then retain all paths that have appeared within five days to try to retain backup paths that appear briefly. Then filter out paths with reserved AS numbers due to routing poisoning (which should not appear in the public Internet) and AS loop paths (multiple non-adjacent identical ASs appearing in the same path).

[0125] The BGP routing path information includes: AS, AS link, VP, the distance between the AS link and the VP, the number of VPs observed to the AS link, and the BGP community value of the AS link.

[0126] (2) Operator information;

[0127] The operator information data includes the mapping between AS and operator, which comes from the aggregation of routing registration information of multiple routing registries by the Center for Applied Internet Data Analysis (CAIDA) of the United States. These routing registries include WHOIS information obtained from five regions (including RIRs: ARIN for North America, LACNIC for South America, RIPE NCC for Europe, AFRINIC for Africa, and APNIC for Asia / Pacific, including Australia) and two national Internet registries (including NIRs: KRNIC for South Korea and JPNIC for Japan). Each AS has a corresponding operating organization, and ASs belonging to the same operating organization are regarded as S2S relationships, and the S2S relationship is not inferred in subsequent inferences.

[0128] (3) AS type information;

[0129] AS type information comes from PeeringDB, the world's largest Internet database. We downloaded and extracted the business type data of AS in PeeringDB, and merged the AS business types of similar businesses and services, that is, "Cable / Digital Subscriber Line / Internet Service Provider", "Network Service Provider" and "Network Service" were merged into "Transmission / Access Provider"; "Government Network", "Education / Research" and "Enterprise Network" were merged into "Enterprise Network"; "Route Collector", "Route Server" and "Non-profit Network" were merged into "Non-profit Network", and "Content Provider" remained "Content Provider".

[0130] Then ignore the "non-profit network" in the merged AS business type data (because the business types of these ASs are unclear and it is meaningless to infer the AS business relationship and hierarchy), and obtain the AS type information of the association relationship diagram, including: "transmission / access provider", "content provider", and "enterprise network".

[0131] Among them, "transmission / access providers" provide traffic exchange and access services on the Internet, "content providers" are suppliers of network content, and "enterprise networks" are mainly customers, and on the Internet they are mainly ASs that use Internet services.

[0132] (4) IXP information;

[0133] IXP is a third-party organization for interconnection and data exchange between different ASs. A large number of ASs establish P2P relationships through IXPs to exchange traffic for free. Therefore, whether they are members of the same IXP is an important basis for inferring AS relationships. The IXP information data used comes from data from PeeringDB, Hurricane Electric, PacketClearning House (PCH), Wikipedia, BGP Looking Glass, and GeoNames, which are summarized by CAIDA.

[0134] The IXP information data includes: the name of the IXP, the traffic exchange infrastructure where it is located, the IPv4 prefix, the network operator and the member AS.

[0135] IXP uses bilateral and multilateral peering. Multilateral peering uses the routing server provided by the IXP as an intermediary to achieve dense peering. The routing server belongs to the AS of the IXP and is filtered out from the AS path.

[0136] (5) The meaning of BGP community value;

[0137] Download data from 18 routing registries listed by the Internet Routing Registry (IRR) and extract BGP community values ​​and their meanings. BGP community value is an optional 32-bit number for BGP messages. The first 16 bits are the AS of the community value, and the last 16 bits represent the meaning of the community value, which is used to encode additional information related to the AS path. The AS business relationship inferred from the meaning of the BGP community value is used as real data to verify the inference results. The information of the network operation website is extracted from PeeringDB, and the import and export rules of the AS in the data of the RIPE NCC database are used to supplement the meaning of the BGP community value.

[0138] Among them, for other BGP community values ​​whose meanings cannot be found, they are added to the association relationship graph (AS links with the same BGP community value are more likely to belong to the same business relationship).

[0139] In addition to the BGP community value that can obtain AS business relationships, some network operators use the routing policy specification language to register routing policies in public data sets, among which the largest data source is the RIPE NCC database. The routing policy specification language defines the rules for AS to import and export routes from neighbors. If AS1 imports ANY from AS2, and AS2 exports ANY from AS1, it is considered that AS2 forwards all traffic for AS1, that is, the business relationship between AS2 and AS1 is P2C.

[0140] In this embodiment, step S2 is specifically as follows:

[0141] S21, determine the information required for inferring AS business relationships, including: neighbor information, observation information, hierarchy information, and coexistence information;

[0142] (1) Neighbor information;

[0143] Neighbor information includes: AS link triplet. AS link triplet is three adjacent links on the same path.

[0144] The purpose of using AS link triples is to satisfy the valley-free property. As long as all the triplets of the path do not violate the valley-free property, the path will not violate the valley-free property. Under the constraint of the valley-free property, the business relationship of the AS links before and after determines the business relationship of the intermediate link with a high probability. If the first AS link of the triple is P2P, then the intermediate AS is most likely P2C. Split all BGP routing paths into triplets. In order to ensure that all ASs have at least one triple, for the first hop of the path, add a NULL as the first of the triple, and for the last hop of the path, add NULL as the last of the triple. Take the ratio of each business relationship of the previous hop and the next hop of the intermediate AS as the probability, and divide the probability into high, medium and low, with values ​​greater than 0.1, 0.01 to 0.1, and less than 0.01.

[0145] (2) Observation information;

[0146] The observed information includes: the number of VPs that observe the AS link, the distance to the VP, and non-path information. The AS is more willing to announce the customer's route rather than the peer and supplier's, which makes the P2C link easy to be observed by more VPs, so the VP number feature is used.

[0147] Among them, the number of VPs observing the AS link greater than or equal to 10 is considered to be observed by a large number of VPs, and the number less than 10 is considered to be observed by a small number of VPs; the distance to the VP less than or equal to 2 is considered to be close to the VP, and the distance greater than 2 is considered to be far away from the VP; non-path information refers to neighbors in the AS topology structure but not appearing in the same path at the same time, which is used to describe the probability of P2P or P2C neighbors.

[0148] (3) Hierarchical information;

[0149] The hierarchical information includes: the location of the AS and the location of the VP.

[0150] Among them, ASs at the same level have similar business strategies, so AS level information can provide constraints for the global perspective. The location of VP is also closely related to the business relationship type of AS. For example, it is difficult for a VP at a high level to observe a P2P link at a low level.

[0151] (4) information about getting along;

[0152] The information about the same network includes: being in the same IXP and being in the same traffic exchange infrastructure.

[0153] Since IXPs and traffic exchange infrastructures are mostly used for AS peer-to-peer traffic exchange, the more IXPs and traffic exchange infrastructures two ASs are in, the more likely they are to be in a peer relationship. In real data, more than 90% of the ASs in P2C links are not in the same IXP or traffic exchange infrastructure, while more than 70% of the ASs in P2P links are in at least one of the same IXPs or traffic exchange infrastructures.

[0154] Bayesian networks can well characterize the association relationship between multiple factors, so this embodiment uses Bayesian networks to infer AS business relationships. Unlike the Toposcope method, this embodiment updates and iterates AS business relationships and hierarchical structures under the global constraint that ASs at the same level have similar business strategies. In addition, this embodiment also adds hierarchical information of AS and VP. The positions of AS and VP in the Internet hierarchy reflect the information of Internet observation points and observed points, and are also a global vision constraint, which can better infer AS business relationships. Since both AS business relationships and AS hierarchical structures are unknown and require further inference, this embodiment roughly divides the AS hierarchy based on the AS topology and node degree method when inferring the AS business relationship for the first time, and only adds the AS hierarchy information inferred by this embodiment in subsequent iterations.

[0155] S22, construct a training set for the Bayesian network;

[0156] First, the data is grouped according to VP, that is, the BGP routing path is divided into N groups according to VP.

[0157] Among them, the data of the observation points belonging to "full VP" (VP that reports a large number of routes to the route collector) are divided into N-1 groups, and the remaining data belonging to "partial VP" (VP that reports a small number of routes to the route collector) are divided into one group. In order to make the distribution of each group of data more even, the level of the VP is considered when grouping the data, and VPs of different levels are evenly divided into each group.

[0158] Then, the AS-Rank method is used to infer the preliminary AS business relationship for each group. Then, the preliminary AS business relationship data of each group are voted by ensemble learning to obtain high-confidence AS business relationships as the training set of the Bayesian network.

[0159] Among them, the preliminary AS business relationship results of each group are voted for integrated learning to obtain trusted links and fuzzy links, and the inferences that are consistent with at least 80% of the groups are regarded as consensus links. When the consensus link is P2P, if no more than 20% of the groups observe the link, it is regarded as a trusted link; when the consensus link is P2C, if at least 80% of the groups observe the link, it is regarded as a trusted link; the trusted link will be used as a training set to obtain the structure of the Bayesian network, and the remaining links except the trusted link are regarded as fuzzy links.

[0160] S23, based on step S22, the Bayesian network structure is obtained, the EM algorithm is used to train the Bayesian network, and finally the AS business relationship is inferred;

[0161] Taking high-confidence AS business relationships as input, the Max-Min Hill Climbing (MMLC) structural learning algorithm is used to obtain the probability distribution between each element. After removing the edges with smaller probabilities, the Bayesian network structure associated with each element is obtained, as shown in Figure 3 As shown, the AS business relationship of the high-confidence trusted link and the Bayesian network structure are then used as input, and the EM algorithm is used to obtain the parameters of the Bayesian network (i.e., the conditional probability corresponding to the Bayesian network structure). Finally, the trained Bayesian network is used to infer the business relationship between the low-confidence ASs obtained by ensemble learning voting.

[0162] The high-confidence AS business relationship is a credible link, and the low-confidence AS business relationship is a fuzzy link. In subsequent iterations, only the EM algorithm needs to be used to update the conditional probability and infer the low-confidence AS business relationship.

[0163] In this embodiment, step S3 is specifically as follows:

[0164] Based on steps S1 and S2, the Internet AS-level topology is divided into five levels: Tier 1, Tier 2, Tier 3, Tier 4, and Tier 5. The inferred order is Tier 5, Tier 4, Tier 1, Tier 2, and Tier 3, as follows:

[0165] (1) Tier 5 is the lowest level AS, which has no customers and does not forward traffic. It includes stub AS and multi-homed AS.

[0166] Among them, a stub AS refers to an AS that is only connected to one AS and has no customers, and a multi-homed AS refers to an AS that is connected to multiple ASs but does not forward traffic.

[0167] (2) Tier 4 is a small local supplier with only a small number of customer ASs;

[0168] Among them, after Tier5 is deleted in the topology, ASs without customers are iteratively searched and deleted, and the deleted AS is Tier4.

[0169] (3) Tier 1 is the top-level densely peered AS without any upstream providers;

[0170] First, use the Bron / kerbosch algorithm to find all clusters, then select the largest cluster with the largest transmission degree (the number of unique neighbors on both sides of the AS link) and no more than 10 ASs, then select other ASs that meet the conditions to add to the largest cluster (that is, at most one AS in the largest cluster is not connected, and it is not on the same path with more than two ASs in the largest cluster at the same time), and finally use the Bron / kerbosch algorithm again to find the largest cluster by summing the transmission degrees. This largest cluster is Tier 1.

[0171] (4) Tier 2 refers to some large regional and national Internet providers;

[0172] These ASes have peer relationships with many Tier 1 ASes, but also need to purchase at least some traffic transit services to access certain parts of the Internet.

[0173] Tier 2 ASs are not inclined to be peers with other Tier 1 ASs. Taking Tier 1 as the initial set, sort the ASs according to the transmission degree and find the smallest set that satisfies weak partitioning. Tier 2 is the AS in the set except Tier 1.

[0174] (5) Tier 3 is a regional access provider that connects with a small number of Tier 1 and Tier 2 to provide network services to the lower-level ASs;

[0175] Tier 3 is the remaining AS after inferring other levels of AS.

[0176] In this embodiment, in step S2, when the AS business relationship is first inferred, the AS hierarchy is roughly divided based on the AS topology and node degree method, as follows:

[0177] First, the AS with degree 1 in the topology is considered as Tier 5, and the AS with degree 2 connected to Tier 5 is considered as Tier 4. Then, the Bron / Kerbosch algorithm is used to find the largest group as Tier 1. Then, the AS connected to Tier 1 with a degree greater than or equal to 100 is considered as Tier 2. Finally, the remaining AS is Tier 3. In subsequent iterations, the AS hierarchy information inferred in step S3 is added.

[0178] In this embodiment, step S4 is specifically as follows:

[0179] The constraint of the global vision is that the roles of ASs at the same level in Internet traffic exchange are similar, and their business strategies are similar. The meta-path of the association relationship graph is used to measure this similarity.

[0180] Among them, the meta-path is all the paths composed of nodes and relationships between two nodes in the association graph. The meta-path in the association graph corresponds to the features in the existing data set, and the semantics under different paths means different similarities. The meta-path between AS links is used to measure the similarity of AS links at the same level and between different levels, so as to illustrate the law of their distribution and reflect the global vision of the similarity of AS business strategies at the same level. Such a global vision can provide new constraints for AS-level topology inference, narrow the solution space, and be more accurate than the existing method using neighbor information.

[0181] When ASs are layered, ASs between adjacent layers are easily confused due to inaccurate AS business relationships or insufficient observations. For example, an AS in Tier 1 may belong to Tier 2, an AS in Tier 2 may belong to Tier 1 or Tier 3, an AS in Tier 3 may belong to Tier 2 and Tier 4, an AS in Tier 4 may belong to Tier 3, and an AS in Tier 5 may belong to Tier 4. Therefore, we can focus on these layers that are prone to confusion to reduce computational complexity.

[0182] Define the AS-level topological association graph G=(V,E), the node type mapping function is φ:V→A, and the edge type mapping function is ψ:E→R.

[0183] Among them, V represents a node, E represents an edge, each v∈V belongs to a special node type φ(v)∈A, and each link e∈E belongs to a special relationship type ψ(e)∈R.

[0184] Using network mode T G =(A,R) is used to describe the meta-level (i.e., schema level) of the AS-level topological association graph, i.e., a directed graph defined on node type A, with edges being relationships from R.

[0185] The concept of network model is similar to the entity relationship model in database system, but only captures entity types and their binary relationships without considering the attributes of each entity type. Analyze the elements related to AS business relationships and hierarchical structures, and define network models such as Figure 4As shown, the network mode includes: AS link, AS, type of AS, IXP where AS is located, traffic exchange infrastructure where AS is located, network operator where AS is located, AS community value marking AS link in BGP path, distance of AS link from observation point (long distance if greater than 2 hops, otherwise short distance), number of VPs observed in AS link (large number of VPs if greater than or equal to 10, otherwise small number of VPs).

[0186] The meta-paths related to the AS business relationship in the network model include: (AS link, AS, IXP, AS, AS link), (AS link, AS, traffic exchange infrastructure, AS, AS link), (AS link, AS, AS type, AS, AS link), (AS link, AS, network operator, AS, AS link), (AS link, BGP community value, AS link), (AS link, long distance, AS link), (AS link, short distance, AS link), (AS link, a large number of VPs, AS link), (AS link, a small number of VPs, AS link).

[0187] like Figure 4 As shown, in the network mode T G =(A,R), the meta-path P in the AS-level topological association graph is represented as Then a node A1 and node A l The compound relationship between

[0188] Among them, A l Indicates the lth node type, R l-1 Represents the l-1th relationship type.

[0189] Pathsim is able to capture the semantics of peer similarity, that is, two similar peer objects should not only be closely connected but also have similar visibility. For a given symmetric meta-path P, the Pathsim metric expression for AS links x and y of the same type is defined as follows:

[0190]

[0191] Among them, p x→y represents the path between x and y, p x→x represents the path between x and x, p y→y represents the path between y and y.

[0192] Based on the Internet AS-level topology association network G = (V, E) and network model T G , meta-path P = (A1A2…A l ), calculate the matrix

[0193] in, Indicates A i and A j The adjacency matrix of ij Represents the value of the i-th row and j-th column of M, that is, the object x under the meta-path P i ∈A1 and x j ∈A l The number of paths between .

[0194] Then from the same type of x i and x j The Pathsim between two objects is

[0195] Among them, M ii and M jj Indicates that given a meta-path in the network x i and x j visibility.

[0196] Then traverse all the levels, for those in (Tier i ,Tier j ) of the link L(i,j)(Tier i The level is not lower than Tier j The Pathsim between P2P links and P2C links is calculated respectively.

[0197] When obtaining the calculation matrix M, the common number of IXPs, BGP community values, and network operators connected to the AS links is calculated, and then normalized to characterize the three types of meta-paths: IXP, BGP community value, and network operator (being in a different IXP does not mean that the two AS links are not similar, but it is reflected based on the overall IXP data, that is, as long as they are in the same IXP, the AS links are similar).

[0198] Then, for a given business relationship c1∈{P2P,P2C}, the average similarity ranking between each AS link and the rest of the AS links is calculated, and the AS links L ranked before Δ are considered similar. Similar (i, j) (i.e., the business strategies of these AS links are similar and they are high-confidence AS links), while the remaining AS links with lower rankings are regarded as dissimilar links L Dissimilar (i, j) (i.e., the business strategies of these AS links are not similar to those of the other AS links and they are AS links with low confidence).

[0199] Among them, Δ is set according to the actual situation.

[0200] Dissimilar AS Link L Dissimilar(i,j) may be a link of another business relationship or belong to another level. Dissimilar (i,j) further judges that the dissimilar AS links L Dissimilar (i,j) and in (Tier i ,Tier j ) calculates the similar links of another business relationship c2∈{P2P,P2C},c1≠c2, and considers the top Δ links as being able to be adjusted, that is, adjusting them to another business relationship c2. Dissimilar (i,j) The remaining unadjusted links, if Tier i If it is not Tier 1, then i-1 ,Tier j ) is calculated based on similar links. If Tier j If not Tier 5, then (Tier i ,Tier j+1 ) are calculated, and the links ranked higher than Δ are considered as adjustable links L Candidate Finally, the AS links that have not been adjusted are no longer adjusted.

[0201] After traversing all levels, determine L Candidate Whether the AS level in the AS needs to be adjusted. For any AS, calculate the number of times that all its associated links need to modify the level. If the number of times that the level needs to be modified is greater than the number that does not need to be modified, then modify the level of the AS.

[0202] In summary, the method of the present invention overcomes the deviation caused by the location and number of observation points in the existing AS-level topology inference methods, organizes the elements related to the AS-level topology through an association relationship diagram, and can better explore the complex association relationships between the various elements. At the same time, under the constraint of the global vision that ASs at the same level of the Internet have similar routing strategies, it can narrow the solution space and achieve the AS-level topology inference accuracy that is difficult to achieve by relying solely on local observations and neighbor information, effectively reduce the deviation caused by the location and number of observation points, and can more accurately infer the Internet AS-level topology.

[0203] Those skilled in the art will appreciate that the above embodiments are intended to help readers understand the principles of the present invention, and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. For those skilled in the art, the present invention may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for inferring Internet autonomous domain-level topology based on a global perspective, the specific steps are as follows: S1. Construct an Internet AS-level topology association diagram; S2, based on step S1, using Bayesian network to infer AS business relationship; S3, based on steps S1 and S2, performing AS hierarchy division; S4, after inferring the AS business relationship and hierarchical structure based on steps S2-S3, perform AS-level topology correction based on the meta-path, that is, add a global view constraint to adjust the AS business relationship and hierarchical structure; S5. Based on step S4, determine the number of AS business relationships and hierarchical structure adjustments. If it is greater than the set threshold, repeat steps S1-S4 with the adjusted hierarchy as input, and continue to infer AS business relationships and AS hierarchical structures until it is less than the set threshold. Stop iteration, output the results, and complete the Internet autonomous domain-level topology inference.

2. According to claim 1, a method for inferring Internet autonomous domain-level topology based on a global perspective is characterized in that: The step S1 is specifically as follows: The nodes in the association relationship graph include: AS, AS link, network operator, IXP, traffic exchange infrastructure and VP; The attributes in the association relationship diagram include: AS attributes and AS link attributes; Among them, AS attributes include AS type and AS hierarchy; AS link attributes include: AS business relationship, distance between AS link and VP, number of VPs observed to AS link, and BGP community value; The nodes of the association relationship graph are associated with each other through different relationships, including: a network operator operates an AS, a network operator operates an IXP, an AS exchanges traffic with an IXP, an AS exchanges traffic with a traffic exchange infrastructure, an IXP is located in a traffic exchange infrastructure, a VP belongs to an AS, an AS link has an active / destination AS, and a VP observes an AS link; The data of the association relationship graph comes from a public data set, including: BGP routing path and routing registration information; The route registration information includes: operator information, AS type information, IXP information, and the meaning of the BGP community value; The data of the association relationship diagram are as follows: (1) BGP routing path; Download all BGP routing paths in the five-day period from Route-Views and RIPE RIS public databases and filter out irrelevant and erroneous paths, and the filtered BGP routing paths are the BGP routing paths of the association relationship graph; The filtering process is as follows: First, extract all AS paths with destinations as IPv4 prefixes, then keep all paths that have appeared within five days to try to keep backup paths that appear briefly, and then filter out paths with reserved AS numbers and AS loop paths caused by routing poisoning, etc. The BGP routing path information includes: AS, AS link, VP, distance between AS link and VP, number of VPs observed to AS link, and BGP community value of AS link; (2) Operator information; The operator information data includes the mapping between AS and operator, which comes from CAIDA. Each AS has a corresponding operation organization. ASs belonging to the same operation organization are considered to be in S2S relationship, and the S2S relationship is not inferred in subsequent inferences. (3) AS type information; Download and extract the business type data of AS in PeeringDB, and merge the business types of ASs with similar businesses and services, that is, "cable / digital subscriber line / Internet service provider", "network service provider" and "network service" are merged into "transmission / access provider"; "government network", "education / research" and "enterprise network" are merged into "enterprise network"; "route collector", "route server" and "non-profit network" are merged into "non-profit network", and "content provider" is still "content provider"; Then, the "non-profit network" in the merged AS business type data is ignored, and the AS type information of the association relationship diagram is obtained, including: "transmission / access provider", "content provider", and "enterprise network"; Among them, "transmission / access providers" provide traffic exchange and access services on the Internet, "content providers" are suppliers of service network content, and "enterprise networks" are mainly customers, and on the Internet they are mainly ASs that use Internet services; (4) IXP information; IXP is a third-party organization that interconnects different ASes and exchanges data. The IXP information data used comes from data from PeeringDB, Hurricane Electric, Packet Clearing House, Wikipedia, BGP Looking Glass, and GeoNames summarized by CAIDA. The IXP information data includes: the name of the IXP, the traffic exchange infrastructure in which it is located, the IPv4 prefix, the network operator and the member AS; IXPs use bilateral and multilateral peering. Multilateral peering uses the route server provided by the IXP as an intermediary to achieve dense peering. The route server belongs to the AS of the IXP and is filtered out of the AS path. (5) The meaning of BGP community value; Download data from 18 routing registries listed in the IRR and extract BGP community values ​​and their meanings; BGP community values ​​are optional for BGP messages and are 32-bit numbers. The first 16 bits are the AS of the community value, and the last 16 bits represent the meaning of the community value, which is used to encode additional information related to the AS path; the AS business relationship inferred from the meaning of the BGP community value is used as real data to verify the inference results; and extract information on network operation websites from PeeringDB and AS import and export rules from the data in the RIPE NCC database as a supplement to the meaning of the BGP community value; Among them, for other BGP community values ​​whose meanings cannot be found, they are added to the association relationship graph.

3. According to the method of claim 1, the method is characterized in that: The step S2 is specifically as follows: S21, determine the information required for inferring AS business relationships, including: neighbor information, observation information, hierarchy information, and coexistence information; (1) Neighbor information; Neighbor information includes: AS link triplet; AS link triplet is three adjacent links on the same path; All BGP routing paths are split into triplets. For the first hop of the path, a NULL is added as the first tuple, and for the last hop of the path, a NULL is added as the last tuple. The ratio of each business relationship between the previous hop and the next hop of the intermediate AS is used as the probability, and the probability is divided into high, medium and low, with values ​​greater than 0.1, 0.01 to 0.1, and less than 0.

01. (2) Observation information; The observed information includes: the number of VPs that observe the AS link, the distance to the VP, and non-path information; Among them, the number of VPs that observe the AS link greater than or equal to 10 is considered to be observed by a large number of VPs, and the number less than 10 is considered to be observed by a small number of VPs; the distance to the VP less than or equal to 2 is considered to be close to the VP, and the distance greater than 2 is considered to be far from the VP; non-path information refers to neighbors in the AS topology structure but not appearing in a path at the same time, which is used to describe the probability of P2P or P2C neighbors; (3) Hierarchical information; The hierarchical information includes: the location of the AS and the location of the VP; Among them, ASs at the same level have similar business strategies, so AS level information can provide constraints from a global perspective; (4) information about getting along; The information about the same IXP includes: the same traffic exchange infrastructure; S22, construct a training set for the Bayesian network; First, the data is grouped according to VP, that is, the BGP routing path is divided into N groups according to VP; The data of observation points belonging to "full VP" are divided into N-1 groups, and the remaining data belonging to "partial VP" are divided into one group. When grouping data, the level of VP is considered, and VPs of different levels are evenly divided into different groups; Then, the AS-Rank method is used to infer the preliminary AS business relationship for each group, and then the preliminary AS business relationship data of each group is voted by ensemble learning to obtain high-confidence AS business relationships as the training set of the Bayesian network; Among them, the preliminary AS business relationship results of each group are integrated and voted to obtain trusted links and fuzzy links, and the inferences that are consistent with at least 80% of the groups are regarded as consensus links; when the consensus link is P2P, if no more than 20% of the groups observe the link, it is regarded as a trusted link; when the consensus link is P2C, if at least 80% of the groups observe the link, it is regarded as a trusted link; except for the trusted link, the remaining links are regarded as fuzzy links; S23, based on step S22, the Bayesian network structure is obtained, the EM algorithm is used to train the Bayesian network, and finally the AS business relationship is inferred; Taking high-confidence AS business relationships as input, the maximum-minimum hill climbing (MMLC) structural learning algorithm is used to obtain the probability distribution between each element. After removing the edges with smaller probabilities, the Bayesian network structure of the association between each element is obtained. Then, taking high-confidence AS business relationships of trusted links and the Bayesian network structure as input, the EM algorithm is used to obtain the parameters of the Bayesian network. Finally, the trained Bayesian network is used to infer the low-confidence AS business relationships obtained by ensemble learning voting. Among them, the high-confidence AS business relationship is a credible link, and the low-confidence business relationship between ASs is a fuzzy link; and in subsequent iterations, only the EM algorithm needs to be used to update the conditional probability and infer the low-confidence AS business relationship.

4. According to the method of claim 1, the method is characterized in that: The step S3 is specifically as follows: Based on steps S1 and S2, the Internet AS-level topology is divided into five levels: Tier 1, Tier 2, Tier 3, Tier 4, and Tier 5; the inferred order is Tier 5, Tier 4, Tier 1, Tier 2, and Tier 3, as follows: (1) Tier 5 is the lowest level AS, which has no customers and does not forward traffic. It includes stub AS and multi-homed AS. Among them, a stub AS is an AS that is only connected to one AS and has no clients, and a multi-homed AS is an AS that is connected to multiple ASes but does not forward traffic; (2) Tier 4 is a small local supplier with only a small number of customer ASs; After Tier5 is deleted from the topology, ASs without customers are iteratively searched and deleted, and the deleted AS is Tier4. (3) Tier 1 is the top-level densely peered AS without any upstream providers; First, use the Bron / kerbosch algorithm to find all the clusters, then select the largest cluster with the largest transmission degree and no more than 10 ASs, then select other ASs that meet the conditions and add them to the largest cluster, and finally use the Bron / kerbosch algorithm again to find the largest cluster by summing the transmission degrees. This largest cluster is Tier 1. (4) Tier 2 refers to some large regional and national Internet providers; Tier 2 ASs do not tend to be peers with other Tier 1 ASs. Taking Tier 1 as the initial set, sort the ASs according to the transmission degree and find the smallest set that satisfies weak partitioning. Tier 2 is the AS in the set except Tier 1. (5) Tier 3 is a regional access provider that connects with a small number of Tier 1 and Tier 2 to provide network services to the lower-level ASs; Tier 3 is the remaining AS after inferring other levels of AS.

5. According to the method of claim 1, the method is characterized in that: The step S4 is specifically as follows: The constraint of the global vision is that the ASs at the same level have similar roles in Internet traffic exchange and similar business strategies, and the meta-path of the association graph is used to measure this similarity; Among them, the meta-path is all the paths composed of nodes and relationships between two nodes in the association graph. The meta-path in the association graph corresponds to the features in the existing data set. The semantics under different paths means different similarities. The meta-path between AS links is used to measure the similarity of AS links at the same level and between different levels. Define the AS-level topological association graph G = (V, E), the node type mapping function is φ: V→A, and the edge type mapping function is ψ: E→R; Where V represents a node, E represents an edge, each v∈V belongs to a special node type φ(v)∈A, and each link e∈E belongs to a special relationship type ψ(e)∈R; Using network mode T G =(A,R) to describe the meta-level of the AS-level topological association graph, i.e., a directed graph defined on node type A, with edges being relationships from R; The network mode includes: AS link, AS, type of AS, IXP where AS is located, traffic exchange infrastructure where AS is located, network operator where AS is located, AS community value marking AS link in BGP path, distance of AS link from observation point (long distance if greater than 2 hops, otherwise short distance), number of VPs observed for AS link (large number of VPs if greater than or equal to 10, otherwise small number of VPs); Then the meta-paths related to the AS business relationship in the network model include: (AS link, AS, IXP, AS, AS link), (AS link, AS, traffic exchange infrastructure, AS, AS link), (AS link, AS, AS type, AS, AS link), (AS link, AS, network operator, AS, AS link), (AS link, BGP community value, AS link), (AS link, long distance, AS link), (AS link, short distance, AS link), (AS link, a large number of VPs, AS link), (AS link, a small number of VPs, AS link); In the network mode T G =(A,R), the meta-path P in the AS-level topology association graph is represented as Then a node A1 and node A l The compound relationship between Among them, A l Indicates the lth node type, R l-1 Indicates the l-1th relationship type; For a given symmetric meta-path P, the Pathsim metric expression for AS links x and y of the same type is defined as follows: Among them, p x→y represents the path between x and y, p x→x represents the path between x and x, p y→y represents the path between y and y; Based on the Internet AS-level topology association network G = (V, E) and network model T G , meta-path P = (A1A2…A l ), calculate the matrix in, Indicates A i and A j The adjacency matrix of ij Represents the value of the i-th row and j-th column of M, that is, the object x under the meta-path P i ∈A1 and x j ∈A l The number of paths between Then from the same type of x i and x j The Pathsim between two objects is Among them, M ii and M jj Indicates that given a meta-path in the network x i and x j visibility; Then traverse all the levels, for those in (Tier i ,Tier j ) of the link L(i,j)(Tier i The level is not lower than Tier j Pathsim between P2P links and P2C links is calculated respectively; When obtaining the calculation matrix M, the common number of IXPs, BGP community values, and network operators connected to the AS links is calculated, and then normalized to characterize the three types of meta-paths: IXP, BGP community value, and network operator; Then, for a given business relationship c1∈{P2P,P2C}, the average similarity ranking between each AS link and the rest of the AS links is calculated, and the AS links L ranked before Δ are considered similar. Similar (i, j), and the remaining AS links with lower rankings are regarded as dissimilar links L Dissimilar (i,j); Among them, Δ is set according to the actual situation; Then for the dissimilar AS link L Dissimilar (i,j) further judges that the dissimilar AS links L Dissimilar (i,j) and in (Tier i ,Tier j ) calculates the similar links of another business relationship c2∈{P2P,P2C},c1≠c2, and considers the top Δ links as being able to be adjusted, that is, adjusting them to another business relationship c2; for these dissimilar AS links L Dissimilar (i,j) The remaining unadjusted links, if Tier i If it is not Tier 1, then i-1 ,Tier j ) is calculated based on similar links. If Tier j If not Tier 5, then (Tier i ,Tier j+1 ) are calculated, and the links ranked higher than Δ are considered as adjustable links L Candidate ; Finally, the AS link that has not been adjusted will not be adjusted; After traversing all levels, determine L Candidate Whether the AS level in the AS needs to be adjusted; for any AS, calculate the number of times that all its associated links need to modify the level. If the number of times that the level needs to be modified is greater than the number that does not need to be modified, the level of the AS is modified.

6. The method for inferring Internet autonomous domain-level topology based on a global perspective according to claim 3, characterized in that: In step S2, when the AS business relationship is first inferred, the AS hierarchy is roughly divided based on the AS topology and node degree method, as follows: First, the AS with degree 1 in the topology is considered as Tier 5, and the AS with degree 2 connected to Tier 5 is considered as Tier 4. Then, the Bron / Kerbosch algorithm is used to find the largest group as Tier 1. Then, the AS connected to Tier 1 with a degree greater than or equal to 100 is considered as Tier 2. Finally, the remaining AS is Tier 3. In subsequent iterations, the AS hierarchy information inferred in step S3 is added.

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