S. K. Kim, Zane Ma, Siddharth Murali, Joshua Mason, Andrew K. Miller, Michael Bailey
Ethereum, the second-largest cryptocurrency valued at a peak of $138 billion in 2018, is a decentralized, Turing-complete computing platform. Although the stability and security of Ethereum---and blockchain systems in general---have been widely-studied, most analysis has focused on application level features of these systems such as cryptographic mining challenges, smart contract semantics, or block mining operators. Little attention has been paid to the underlying peer-to-peer (P2P) networks that are responsible for information propagation and that enable blockchain consensus. In this work, we develop NodeFinder to measure this previously opaque network at scale and illuminate the properties of its nodes. We analyze the Ethereum network from two vantage points: a three-month long view of nodes on the P2P network, and a single day snapshot of the Ethereum Mainnet peers. We uncover a noisy DEVp2p ecosystem in which fewer than half of all nodes contribute to the Ethereum Mainnet. Through a comparison with other previously studied P2P networks including BitTorrent, Gnutella, and Bitcoin, we find that Ethereum differs in both network size and geographical distribution.
{"title":"Measuring Ethereum Network Peers","authors":"S. K. Kim, Zane Ma, Siddharth Murali, Joshua Mason, Andrew K. Miller, Michael Bailey","doi":"10.1145/3278532.3278542","DOIUrl":"https://doi.org/10.1145/3278532.3278542","url":null,"abstract":"Ethereum, the second-largest cryptocurrency valued at a peak of $138 billion in 2018, is a decentralized, Turing-complete computing platform. Although the stability and security of Ethereum---and blockchain systems in general---have been widely-studied, most analysis has focused on application level features of these systems such as cryptographic mining challenges, smart contract semantics, or block mining operators. Little attention has been paid to the underlying peer-to-peer (P2P) networks that are responsible for information propagation and that enable blockchain consensus. In this work, we develop NodeFinder to measure this previously opaque network at scale and illuminate the properties of its nodes. We analyze the Ethereum network from two vantage points: a three-month long view of nodes on the P2P network, and a single day snapshot of the Ethereum Mainnet peers. We uncover a noisy DEVp2p ecosystem in which fewer than half of all nodes contribute to the Ethereum Mainnet. Through a comparison with other previously studied P2P networks including BitTorrent, Gnutella, and Bitcoin, we find that Ethereum differs in both network size and geographical distribution.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74567505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ethereum is the second most valuable cryptocurrency today, with a current market cap of over $68B. What sets Ethereum apart from other cryptocurrencies is that it uses the blockchain to not only store a record of transactions, but also smart contracts and a history of calls made to those contracts. Thus, Ethereum represents a new form of distributed system: one where users can implement contracts that can provide functionality such as voting protocols, crowdfunding projects, betting agreements, and many more. However, despite the massive investment, little is known about how contracts in Ethereum are actually created and used. In this paper, we examine how contracts in Ethereum are created, and how users and contracts interact with one another. We modify the geth client to log all such interactions, and find that contracts today are three times more likely to be created by other contracts than they are by users, and that over 60% of contracts have never been interacted with. Additionally, we obtain the bytecode of all contracts and look for similarity; we find that less than 10% of user-created contracts are unique, and less than 1% of contract-created contracts are so. Clustering the contracts based on code similarity reveals even further similarity. These results indicate that there is substantial code re-use in Ethereum, suggesting that bugs in such contracts could have wide-spread impact on the Ethereum user population.
{"title":"Analyzing Ethereum's Contract Topology","authors":"Lucianna Kiffer, Dave Levin, A. Mislove","doi":"10.1145/3278532.3278575","DOIUrl":"https://doi.org/10.1145/3278532.3278575","url":null,"abstract":"Ethereum is the second most valuable cryptocurrency today, with a current market cap of over $68B. What sets Ethereum apart from other cryptocurrencies is that it uses the blockchain to not only store a record of transactions, but also smart contracts and a history of calls made to those contracts. Thus, Ethereum represents a new form of distributed system: one where users can implement contracts that can provide functionality such as voting protocols, crowdfunding projects, betting agreements, and many more. However, despite the massive investment, little is known about how contracts in Ethereum are actually created and used. In this paper, we examine how contracts in Ethereum are created, and how users and contracts interact with one another. We modify the geth client to log all such interactions, and find that contracts today are three times more likely to be created by other contracts than they are by users, and that over 60% of contracts have never been interacted with. Additionally, we obtain the bytecode of all contracts and look for similarity; we find that less than 10% of user-created contracts are unique, and less than 1% of contract-created contracts are so. Clustering the contracts based on code similarity reveals even further similarity. These results indicate that there is substantial code re-use in Ethereum, suggesting that bugs in such contracts could have wide-spread impact on the Ethereum user population.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80832351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Taha Khan, Joe DeBlasio, G. Voelker, A. Snoeren, Chris Kanich, N. Vallina-Rodriguez
Global Internet users increasingly rely on virtual private network (VPN) services to preserve their privacy, circumvent censorship, and access geo-filtered content. Due to their own lack of technical sophistication and the opaque nature of VPN clients, however, the vast majority of users have limited means to verify a given VPN service's claims along any of these dimensions. We design an active measurement system to test various infrastructural and privacy aspects of VPN services and evaluate 62 commercial providers. Our results suggest that while commercial VPN services seem, on the whole, less likely to intercept or tamper with user traffic than other, previously studied forms of traffic proxying, many VPNs do leak user traffic---perhaps inadvertently---through a variety of means. We also find that a non-trivial fraction of VPN providers transparently proxy traffic, and many misrepresent the physical location of their vantage points: 5--30% of the vantage points, associated with 10% of the providers we study, appear to be hosted on servers located in countries other than those advertised to users.
{"title":"An Empirical Analysis of the Commercial VPN Ecosystem","authors":"Mohammad Taha Khan, Joe DeBlasio, G. Voelker, A. Snoeren, Chris Kanich, N. Vallina-Rodriguez","doi":"10.1145/3278532.3278570","DOIUrl":"https://doi.org/10.1145/3278532.3278570","url":null,"abstract":"Global Internet users increasingly rely on virtual private network (VPN) services to preserve their privacy, circumvent censorship, and access geo-filtered content. Due to their own lack of technical sophistication and the opaque nature of VPN clients, however, the vast majority of users have limited means to verify a given VPN service's claims along any of these dimensions. We design an active measurement system to test various infrastructural and privacy aspects of VPN services and evaluate 62 commercial providers. Our results suggest that while commercial VPN services seem, on the whole, less likely to intercept or tamper with user traffic than other, previously studied forms of traffic proxying, many VPNs do leak user traffic---perhaps inadvertently---through a variety of means. We also find that a non-trivial fraction of VPN providers transparently proxy traffic, and many misrepresent the physical location of their vantage points: 5--30% of the vantage points, associated with 10% of the providers we study, appear to be hosted on servers located in countries other than those advertised to users.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91348976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DNS backscatter detects internet-wide activity by looking for common reverse DNS lookups at authoritative DNS servers that are high in the DNS hierarchy. Both DNS backscatter and monitoring unused address space (darknets or network telescopes) can detect scanning in IPv4, but with IPv6's vastly larger address space, darknets become much less effective. This paper shows how to adapt DNS backscatter to IPv6. IPv6 requires new classification rules, but these reveal large network services, from cloud providers and CDNs to specific services such as NTP and mail. DNS backscatter also identifies router interfaces suggesting traceroute-based topology studies. We identify 16 scanners per week from DNS backscatter using observations from the B-root DNS server, with confirmation from backbone traffic observations or blacklists. After eliminating benign services, we classify another 95 originators in DNS backscatter as potential abuse. Our work also confirms that IPv6 appears to be less carefully monitored than IPv4.
{"title":"Who Knocks at the IPv6 Door?: Detecting IPv6 Scanning","authors":"K. Fukuda, J. Heidemann","doi":"10.1145/3278532.3278553","DOIUrl":"https://doi.org/10.1145/3278532.3278553","url":null,"abstract":"DNS backscatter detects internet-wide activity by looking for common reverse DNS lookups at authoritative DNS servers that are high in the DNS hierarchy. Both DNS backscatter and monitoring unused address space (darknets or network telescopes) can detect scanning in IPv4, but with IPv6's vastly larger address space, darknets become much less effective. This paper shows how to adapt DNS backscatter to IPv6. IPv6 requires new classification rules, but these reveal large network services, from cloud providers and CDNs to specific services such as NTP and mail. DNS backscatter also identifies router interfaces suggesting traceroute-based topology studies. We identify 16 scanners per week from DNS backscatter using observations from the B-root DNS server, with confirmation from backbone traffic observations or blacklists. After eliminating benign services, we classify another 95 originators in DNS backscatter as potential abuse. Our work also confirms that IPv6 appears to be less carefully monitored than IPv4.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87381979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arjun Roy, D. Bansal, David Brumley, H. Chandrappa, Parag Sharma, Rishabh Tewari, Behnaz Arzani, A. Snoeren
Cloud customers require highly reliable and performant leased datacenter infrastructure to deliver quality service for their users. It is thus critical for cloud providers to quickly detect and mitigate infrastructure faults. While much is known about managing faults that arise in the datacenter physical infrastructure (i.e., network and server equipment), comparatively little has been published regarding management of the logical overlay networks frequently employed to provide strong isolation in multi-tenant datacenters. We present a first look into the nuances of monitoring these "virtualized" networks through the lens of a large cloud provider. We describe challenges to building cloud-based fault monitoring systems, and use the output of a production system to illuminate how virtualization impacts multi-tenant datacenter fault management. We show that interactions between the virtualization, tenant software, and lower layers of the network fabric both simplify and complicate different aspects of fault detection and diagnosis efforts.
{"title":"Cloud Datacenter SDN Monitoring: Experiences and Challenges","authors":"Arjun Roy, D. Bansal, David Brumley, H. Chandrappa, Parag Sharma, Rishabh Tewari, Behnaz Arzani, A. Snoeren","doi":"10.1145/3278532.3278572","DOIUrl":"https://doi.org/10.1145/3278532.3278572","url":null,"abstract":"Cloud customers require highly reliable and performant leased datacenter infrastructure to deliver quality service for their users. It is thus critical for cloud providers to quickly detect and mitigate infrastructure faults. While much is known about managing faults that arise in the datacenter physical infrastructure (i.e., network and server equipment), comparatively little has been published regarding management of the logical overlay networks frequently employed to provide strong isolation in multi-tenant datacenters. We present a first look into the nuances of monitoring these \"virtualized\" networks through the lens of a large cloud provider. We describe challenges to building cloud-based fault monitoring systems, and use the output of a production system to illuminate how virtualization impacts multi-tenant datacenter fault management. We show that interactions between the virtualization, tenant software, and lower layers of the network fabric both simplify and complicate different aspects of fault detection and diagnosis efforts.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78294896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tobias Lauinger, A. S. Buyukkayhan, A. Chaabane, William K. Robertson, E. Kirda
When desirable Internet domain names expire, they are often re-registered in the very moment the old registration is deleted, in a highly competitive and resource-intensive practice called domain drop-catching. To date, there has been little insight into the daily time period when expired domain names are deleted, and the race to re-registration that takes place. In this paper, we show that .com domains are deleted in a predictable order, and propose a model to infer the earliest possible time a domain could have been re-registered. We leverage this model to characterise at a precision of seconds how fast certain types of domain names are re-registered. We show that 9.5 % of deleted domains are re-registered with a delay of zero seconds. Domains not taken immediately by the drop-catch services are often re-registered later, with different behaviours over the following seconds, minutes and hours. Since these behaviours imply different effort and price points, our methodology can be useful for future work to explain the uses of re-registered domains.
{"title":"From Deletion to Re-Registration in Zero Seconds: Domain Registrar Behaviour During the Drop","authors":"Tobias Lauinger, A. S. Buyukkayhan, A. Chaabane, William K. Robertson, E. Kirda","doi":"10.1145/3278532.3278560","DOIUrl":"https://doi.org/10.1145/3278532.3278560","url":null,"abstract":"When desirable Internet domain names expire, they are often re-registered in the very moment the old registration is deleted, in a highly competitive and resource-intensive practice called domain drop-catching. To date, there has been little insight into the daily time period when expired domain names are deleted, and the race to re-registration that takes place. In this paper, we show that .com domains are deleted in a predictable order, and propose a model to infer the earliest possible time a domain could have been re-registered. We leverage this model to characterise at a precision of seconds how fast certain types of domain names are re-registered. We show that 9.5 % of deleted domains are re-registered with a delay of zero seconds. Domains not taken immediately by the drop-catch services are often re-registered later, with different behaviours over the following seconds, minutes and hours. Since these behaviours imply different effort and price points, our methodology can be useful for future work to explain the uses of re-registered domains.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76293542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pushing software updates to millions of geographically diverse clients is an important technical challenge for software providers. In this paper, we characterize how content delivery networks (CDNs) are used to deliver software updates of two prominent operating systems (Windows and iOS), over a span of 3 years. We leverage a data set of DNS and ping measurements from 9,000 RIPE Atlas clients, distributed across 206 countries, to understand regional and temporal trends in the use of multiple CDNs for delivering OS updates. We contrast two competing methodologies for distributing OS updates employed by Microsoft and Apple, where the majority of Microsoft clients download Windows updates from their local ISP. But, 90% of Apple clients access iOS updates from Apple's own network. We find an approximate improvement of 70 ms in the latency observed by clients in Asia and Africa when accessing content from edge caches in local ISPs. Additionally, Microsoft provides lower latencies to its clients in developing regions by directing them to Akamai's rich network of edge caches. We also observe that clients in developing regions accessing Windows updates from Level 3 get poor latencies arising from the absence of Level 3's footprint in those regions.
{"title":"Characterizing the Deployment and Performance of Multi-CDNs","authors":"Rachee Singh, Arun Dunna, Phillipa Gill","doi":"10.1145/3278532.3278548","DOIUrl":"https://doi.org/10.1145/3278532.3278548","url":null,"abstract":"Pushing software updates to millions of geographically diverse clients is an important technical challenge for software providers. In this paper, we characterize how content delivery networks (CDNs) are used to deliver software updates of two prominent operating systems (Windows and iOS), over a span of 3 years. We leverage a data set of DNS and ping measurements from 9,000 RIPE Atlas clients, distributed across 206 countries, to understand regional and temporal trends in the use of multiple CDNs for delivering OS updates. We contrast two competing methodologies for distributing OS updates employed by Microsoft and Apple, where the majority of Microsoft clients download Windows updates from their local ISP. But, 90% of Apple clients access iOS updates from Apple's own network. We find an approximate improvement of 70 ms in the latency observed by clients in Asia and Africa when accessing content from edge caches in local ISPs. Additionally, Microsoft provides lower latencies to its clients in developing regions by directing them to Akamai's rich network of edge caches. We also observe that clients in developing regions accessing Windows updates from Level 3 get poor latencies arising from the absence of Level 3's footprint in those regions.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88844870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a framework to characterize Internet hosts using deep learning, using Internet scan data to produce numerical and lightweight (low-dimensional) representations of hosts. To do so we first develop a novel method for extracting binary tags from structured texts, the format of the scan data. We then use a variational autoencoder, an unsupervised neural network model, to construct low-dimensional embeddings of our high-dimensional binary representations. We show that these lightweight embeddings retain most of the information in our binary representations, while drastically reducing memory and computational requirements for large-scale analysis. These embeddings are also universal, in that the process used to generate them is unsupervised and does not rely on specific applications. This universality makes the embeddings broadly applicable to a variety of learning tasks whereby they can be used as input features. We present two such examples, (1) detecting and predicting malicious hosts, and (2) unmasking hidden host attributes, and compare the trained models in their performance, speed, robustness, and interpretability. We show that our embeddings can achieve high accuracy (>95%) for these learning tasks, while being fast enough to enable host-level analysis at scale.
{"title":"Characterizing the Internet Host Population Using Deep Learning: A Universal and Lightweight Numerical Embedding","authors":"Armin Sarabi, M. Liu","doi":"10.1145/3278532.3278545","DOIUrl":"https://doi.org/10.1145/3278532.3278545","url":null,"abstract":"In this paper, we present a framework to characterize Internet hosts using deep learning, using Internet scan data to produce numerical and lightweight (low-dimensional) representations of hosts. To do so we first develop a novel method for extracting binary tags from structured texts, the format of the scan data. We then use a variational autoencoder, an unsupervised neural network model, to construct low-dimensional embeddings of our high-dimensional binary representations. We show that these lightweight embeddings retain most of the information in our binary representations, while drastically reducing memory and computational requirements for large-scale analysis. These embeddings are also universal, in that the process used to generate them is unsupervised and does not rely on specific applications. This universality makes the embeddings broadly applicable to a variety of learning tasks whereby they can be used as input features. We present two such examples, (1) detecting and predicting malicious hosts, and (2) unmasking hidden host attributes, and compare the trained models in their performance, speed, robustness, and interpretability. We show that our embeddings can achieve high accuracy (>95%) for these learning tasks, while being fast enough to enable host-level analysis at scale.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77157578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Piet De Vaere, Tobias Bühler, M. Kühlewind, B. Trammell
Passive measurement is a commonly used approach for measuring round trip time (RTT), as it reduces bandwidth overhead compared to large-scale active measurements. However, passive RTT measurement is limited to transport-specific approaches, such as those that utilize Transmission Control Protocol (TCP) timestamps. Furthermore, the continuing deployment of encrypted transport protocols such as QUIC hides the information used for passive RTT measurement from the network. In this work, we introduce the latency spin signal as a lightweight, transport-independent and explicit replacement for TCP timestamps for passive latency measurement. This signal supports per-flow, single-point and single direction passive measurement of end-to-end RTT using just three bits in the transport protocol header, leveraging the existing dynamics of the vast majority of Internet-deployed transports. We show how the signal applies to measurement of both TCP and to QUIC through implementation of the signal in endpoint transport stacks. We also provide a high-performance measurement implementation for the signal using the Vector Packet Processing (VPP) framework. Evaluation on emulated networks and in an Internet testbed demonstrate the viability of the signal, and show that it is resistant to even large amounts of loss or reordering on the measured path.
{"title":"Three Bits Suffice: Explicit Support for Passive Measurement of Internet Latency in QUIC and TCP","authors":"Piet De Vaere, Tobias Bühler, M. Kühlewind, B. Trammell","doi":"10.1145/3278532.3278535","DOIUrl":"https://doi.org/10.1145/3278532.3278535","url":null,"abstract":"Passive measurement is a commonly used approach for measuring round trip time (RTT), as it reduces bandwidth overhead compared to large-scale active measurements. However, passive RTT measurement is limited to transport-specific approaches, such as those that utilize Transmission Control Protocol (TCP) timestamps. Furthermore, the continuing deployment of encrypted transport protocols such as QUIC hides the information used for passive RTT measurement from the network. In this work, we introduce the latency spin signal as a lightweight, transport-independent and explicit replacement for TCP timestamps for passive latency measurement. This signal supports per-flow, single-point and single direction passive measurement of end-to-end RTT using just three bits in the transport protocol header, leveraging the existing dynamics of the vast majority of Internet-deployed transports. We show how the signal applies to measurement of both TCP and to QUIC through implementation of the signal in endpoint transport stacks. We also provide a high-performance measurement implementation for the signal using the Vector Packet Processing (VPP) framework. Evaluation on emulated networks and in an Internet testbed demonstrate the viability of the signal, and show that it is resistant to even large amounts of loss or reordering on the measured path.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82195673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Florian Streibelt, F. Lichtblau, Robert Beverly, A. Feldmann, C. Pelsser, Georgios Smaragdakis, R. Bush
BGP communities are a mechanism widely used by operators to manage policy, mitigate attacks, and engineer traffic; e.g., to drop unwanted traffic, filter announcements, adjust local preference, and prepend paths to influence peer selection. Unfortunately, we show that BGP communities can be exploited by remote parties to influence routing in unintended ways. The BGP community-based vulnerabilities we expose are enabled by a combination of complex policies, error-prone configurations, a lack of cryptographic integrity and authenticity over communities, and the wide extent of community propagation. Due in part to their ill-defined semantics, BGP communities are often propagated far further than a single routing hop, even though their intended scope is typically limited to nearby ASes. Indeed, we find 14% of transit ASes forward received BGP communities onward. Given the rich inter-connectivity of transit ASes, this means that communities effectively propagate globally. As a consequence, remote adversaries can use BGP communities to trigger remote blackholing, steer traffic, and manipulate routes even without prefix hijacking. We highlight examples of these attacks via scenarios that we tested and measured both in the lab as well as in the wild. While we suggest what can be done to mitigate such ill effects, it is up to the Internet operations community whether to take up the suggestions.
{"title":"BGP Communities: Even more Worms in the Routing Can","authors":"Florian Streibelt, F. Lichtblau, Robert Beverly, A. Feldmann, C. Pelsser, Georgios Smaragdakis, R. Bush","doi":"10.1145/3278532.3278557","DOIUrl":"https://doi.org/10.1145/3278532.3278557","url":null,"abstract":"BGP communities are a mechanism widely used by operators to manage policy, mitigate attacks, and engineer traffic; e.g., to drop unwanted traffic, filter announcements, adjust local preference, and prepend paths to influence peer selection. Unfortunately, we show that BGP communities can be exploited by remote parties to influence routing in unintended ways. The BGP community-based vulnerabilities we expose are enabled by a combination of complex policies, error-prone configurations, a lack of cryptographic integrity and authenticity over communities, and the wide extent of community propagation. Due in part to their ill-defined semantics, BGP communities are often propagated far further than a single routing hop, even though their intended scope is typically limited to nearby ASes. Indeed, we find 14% of transit ASes forward received BGP communities onward. Given the rich inter-connectivity of transit ASes, this means that communities effectively propagate globally. As a consequence, remote adversaries can use BGP communities to trigger remote blackholing, steer traffic, and manipulate routes even without prefix hijacking. We highlight examples of these attacks via scenarios that we tested and measured both in the lab as well as in the wild. While we suggest what can be done to mitigate such ill effects, it is up to the Internet operations community whether to take up the suggestions.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80691785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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