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Uncategorized

the tsuNAME vulnerability in DNS

On 2020-05-06, researchers at SIDN Labs, (the .nl registry), InternetNZ (the .nz registry) , and at the Information Science Institute at the University of Southern California publicly disclosed tsuNAME, a vulnerability in some DNS resolver software that can be weaponized to carry out DDoS attacks against authoritative DNS servers.

TsuNAME is a problem that results from cyclic dependencies in DNS records, where two NS records point at each other. We found that some recursive resolvers would follow this cycle, greatly amplifying an initial queries and stresses the authoritative servers providing those records.

Our technical report describes a tsuNAME related event observed in 2020 at the .nz authoritative servers, when two domains were misconfigured with cyclic dependencies. It caused the total traffic to growth by 50%. In the report, we show how an EU-based ccTLD experienced a 10x traffic growth due to cyclic dependent misconfigurations.

We refer DNS operators and developers to our security advisory that provides recommendations for how to mitigate or detect tsuNAME.

We have also created a tool, CycleHunter, for detecting cyclic dependencies in DNS zones. Following responsible disclosure practices, we provided operators and software vendors time to address the problem first. We are happy that Google public DNS and Cisco OpenDNS both took steps to protect their public resolvers, and that PowerDNS and NLnet have confirmed their current software is not affected.

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Uncategorized

congratulations to Xaio Song for receiving a 2021 USC Viterbi award for MS Student Research

Congratulations to Xiao Song for receiving a 2021 USC Viterbi School of Engineering award for Masters Student Research in the Computer Science Department. This award was on the basis of her work observing work-from-home due to Covid-19, as reported in her poster at the NSF PREPARE-VO Workshop and our arXive technical report.

The award was presented at the May 2021 Viterbi Masters Student Awards Ceremony.

Categories
Data Papers Publications

New paper “Auditing for Discrimination in Algorithms Delivering Job Ads” at TheWebConf 2021

We published a new paper “Auditing for Discrimination in Algorithms Delivering Job Ads” by Basileal Imana (University of Southern California), Aleksandra Korolova (University of Southern California) and John Heidemann (University of Southern California/ISI) at TheWebConf 2021 (WWW ’21).

From the abstract:

Skew in the delivery of real-world ads on Facebook (FB) but not LinkedIn (LI).
Comparison of ad delivery using “Reach” (R) and “Conversion” (C) campaign objectives on Facebook. There is skew for both cases but less skew for “Reach”.

Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through their targeted advertising. However, multiple studies have shown that ad delivery on such platforms can be skewed by gender or race due to hidden algorithmic optimization by the platforms, even when not requested by the advertisers. Building on prior work measuring skew in ad delivery, we develop a new methodology for black-box auditing of algorithms for discrimination in the delivery of job advertisements. Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race, from skew due to differences in qualification among people in the targeted audience. This distinction is important in U.S. law, where ads may be targeted based on qualifications, but not on protected categories. Second, we develop an auditing methodology that distinguishes between skew explainable by differences in qualifications from other factors, such as the ad platform’s optimization for engagement or training its algorithms on biased data. Our method controls for job qualification by comparing ad delivery of two concurrent ads for similar jobs, but for a pair of companies with different de facto gender distributions of employees. We describe the careful statistical tests that establish evidence of non-qualification skew in the results. Third, we apply our proposed methodology to two prominent targeted advertising platforms for job ads: Facebook and LinkedIn. We confirm skew by gender in ad delivery on Facebook, and show that it cannot be justified by differences in qualifications. We fail to find skew in ad delivery on LinkedIn. Finally, we suggest improvements to ad platform practices that could make external auditing of their algorithms in the public interest more feasible and accurate.

This paper was awarded runner-up for best student paper at The Web Conference 2021.

The data from this paper is upon request, please see our dataset page.

This work was reported in the popular press: The InterceptMIT Technology ReviewWall Street JournalThe RegisterVentureBeatReutersThe VergeEngadgetAssociated Press.

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Uncategorized

congratulations to Abdul Qadeer for his PhD

I would like to congratulate Dr. Abdul Qadeer for defending his PhD at the University of Southern California in March 2021 and completing his doctoral dissertation “Efficient Processing of Streaming Data in Multi-User and Multi-Abstraction Workflows”.

From the abstract:

Abdul Qadeer after his defense.

Ever-increasing data and evolving processing needs force enterprises to scale-out expensive computational resources to prioritize processing for timely results. Teams process their organization’s data either independently or using ad hoc sharing mechanisms. Often different users start with the same data and the same initial stages (decrypt, decompress, clean, anonymize). As their workflows evolve, later stages often diverge, and different stages may work best with different abstractions. The result is workflows with some overlap, some variations, and multiple transitions where data handling changes between continuous, windowed, and per-block. The system processing this diverse, multi-user, multi-abstraction workflow should be efficient and safe, but also must cope with fault recovery.

Analytics from multiple users can cause redundant processing and data, or encounter performance anomalies due to skew. Skew arises due to static or dynamic imbalance in the workflow stages. Both redundancy and skew waste compute resources and add latency to results. When users bridge between multiple abstractions, such as from per-block processing to windowed processing, they often employ custom code. These transitions can be error prone due to corner cases, can easily add latency as an inefficiency, and custom code is often a source of errors and maintenance difficulty. We need new solutions to manage the above challenges and to expose opportunities for data sharing explicitly. Our thesis is: new methods enable efficient processing of multi-user and multi-abstraction workflows of streaming data. We present two new methods for efficient stream processing—optimizations for multi-user workflows, and multiple abstractions for application coverage and efficient bridging.

These algorithms use a pipeline-graph to detect duplication of code and data across multiple users and cleanly delineate workflow stages for skew management. The pipeline-graph is our job description language that allows developers to specify their need easily and enables our system to automatically detect duplication and manage skew. The pipeline-graph acts as a shared canvas for collaboration amongst users to extend each other’s work. To efficiently implement our deduplication and skew management algorithms, we present streaming data to processing stages as fixed-sized but large blocks. Large-blocks have low meta-data overhead per user, provide good parallelism, and help with fault recovery.

Our second method enables applications to use a different abstraction on a different workflow stage. We provide three key abstractions and show that they cover many classes of analytics and our framework can bridge them efficiently. We provide Block-Streaming, Windowed-Streaming, and Stateful-Streaming abstractions. Block-Streaming is suitable for single-pass applications that care about temporal or spatial locality. Windowed-Streaming allows applications to process accumulated data (time-aligned blocks to sync with external information) and reductions like summation, averages, or other MapReduce-style analytics. We believe our three abstractions allow many classes of analytics and enable processing of one block, many blocks, or infinite stream. Plumb allows multiple abstractions in different parts of the workflow and provides efficient bridging between them so that users could make complex analytics from individual stages without worrying about data movement.

Our methods aim for good throughput, low latency, and clean and easy-to-use support for more applications to achieve better efficiency than our prior hand-tuned but often brittle system. The Plumb framework is the implementation of our solutions and a testbed to validate them. We use real-world workloads from the B-Root DNS domain to demonstrate effectiveness of our solutions. Our processing deduplication increases throughput up to $6\times$, reduces storage by 75%, as compared to their pre-Plumb counterparts. Plumb reduces CPU wastage due to structural skew up to half and reduces latency due to computational skew by 50%. Plumb has cut per-block latency by 74% and latency of daily statistics by 97%, while reducing code size by 58% and lowering manual intervention to handle problems by 73% as compared to pre-Plumb system.

The operational use of Plumb for the B-Root service provides a multi-year validation of our design choices under many traffic conditions. Over the last three years, Plumb has processed more than 12PB of DNS packet data and daily statistics. We show that our abstractions apply to many applications in the domain of networking big-data and beyond.

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Students Uncategorized

congratulations to Manaf Gharaibeh for his PhD

I would like to congratulate Dr. Manaf Gharaibeh for defending his PhD at Colorado State University in February 2020 and completing his doctoral dissertation “Characterizing the Visible Address Space to Enable Efficient, Continuous IP Geolocation” in March 2020.

From the abstract:

Manaf Gharaibeh’s phd defense, with Christos Papadopoulos.

Internet Protocol (IP) geolocation is vital for location-dependent applications and many network research problems. The benefits to applications include enabling content customization, proximal server selection, and management of digital rights based on the location of users, to name a few. The benefits to networking research include providing geographic context useful for several purposes, such as to study the geographic deployment of Internet resources, bind cloud data to a location, and to study censorship and monitoring, among others.
The measurement-based IP geolocation is widely considered as the state-of-the-art client-independent approach to estimate the location of an IP address. However, full measurement-based geolocation is prohibitive when applied continuously to the entire Internet to maintain up-to-date IP-to-location mappings. Furthermore, many IP address blocks rarely move, making it unnecessary to perform such full geolocation.
The thesis of this dissertation states that \emph{we can enable efficient, continuous IP geolocation by identifying clusters of co-located IP addresses and their location stability from latency observations.} In this statement, a cluster indicates a group of an arbitrary number of adjacent co-located IP addresses (a few up to a /16). Location stability indicates a measure of how often an IP block changes location. We gain efficiency by allowing IP geolocation systems to geolocate IP addresses as units, and by detecting when a geolocation update is required, optimizations not explored in prior work. We present several studies to support this thesis statement.
We first present a study to evaluate the reliability of router geolocation in popular geolocation services, complementing prior work that evaluates end-hosts geolocation in such services. The results show the limitations of these services and the need for better solutions, motivating our work to enable more accurate approaches. Second, we present a method to identify clusters of \emph{co-located} IP addresses by the similarity in their latency. Identifying such clusters allows us to geolocate them efficiently as units without compromising accuracy. Third, we present an efficient delay-based method to identify IP blocks that move over time, allowing us to recognize when geolocation updates are needed and avoid frequent geolocation of the entire Internet to maintain up-to-date geolocation. In our final study, we present a method to identify cellular blocks by their distinctive variation in latency compared to WiFi and wired blocks. Our method to identify cellular blocks allows a better interpretation of their latency estimates and to study their geographic properties without the need for proprietary data from operators or users.

Categories
Presentations Publications

new poster “Measuring the Internet during Covid-19 to Evaluate Work-from-Home” at the NSF PREPARE-VO Workshop

Xiao Song presented the poster “Measuring the Internet during Covid-19 to Evaluate Work-from-Home (poster)” at the NSF PREPARE-VO Workshop on 2020-12-15. Xiao describes the poster in our video.

A case study network showing network changes as a result of work-from-home. Here we know ground truth and can see weekly work behavior (the groups of five bumps), followed by changes on the right in March when work-from-home begins.

There was no formal abstract, but this poster presents early results from examining Internet address changes to identify work-from-home resulting from Covid-19.

This work is part of the MINCEQ project, supported as an NSF CISE RAPID, NSF-2028279.

Categories
Publications Students

congratulations to Lan Wei for her new PhD

I would like to congratulate Dr. Lan Wei for defending her PhD in September 2020 and completing her doctoral dissertation “Anycast Stability, Security and Latency in The Domain Name System (DNS) and Content Deliver Networks (CDNs)” in December 2020.

From the abstract:

Clients’ performance is important for both Content-Delivery Networks (CDNs) and the Domain Name System (DNS). Operators would like the service to meet expectations of their users. CDNs providing stable connections will prevent users from experiencing downloading pause from connection breaks. Users expect DNS traffic to be secure without being intercepted or injected. Both CDN and DNS operators care about a short network latency, since users can become frustrated by slow replies.


Many CDNs and DNS services (such as the DNS root) use IP anycast to bring content closer to users. Anycast-based services announce the same IP address(es) from globally distributed sites. In an anycast infrastructure, Internet routing protocols will direct users to a nearby site naturally. The path between a user and an anycast site is formed on a hop-to-hop basis—at each hop} (a network device such as a router), routing protocols like Border Gateway Protocol (BGP) makes the decision about which next hop to go to. ISPs at each hop will impose their routing policies to influence BGP’s decisions. Without globally knowing (also unable to modify) the distributed information of BGP routing table of every ISP on the path, anycast infrastructure operators are unable to predict and control in real-time which specific site a user will visit and what the routing path will look like. Also, any change in routing policy along the path may change both the path and the site visited by a user. We refer to such minimal control over routing towards an anycast service, the uncertainty of anycast routing. Using anycast spares extra traffic management to map users to sites, but can operators provide a good anycast-based service without precise control over the routing?


This routing uncertainty raises three concerns: routing can change, breaking connections; uncertainty about global routing means spoofing can go undetected, and lack of knowledge of global routing can lead to suboptimal latency. In this thesis, we show how we confirm the stability, how we confirm the security, and how we improve the latency of anycast to answer these three concerns. First, routing changes can cause users to switch sites, and therefore break a stateful connection such as a TCP connection immediately. We study routing stability and demonstrate that connections in anycast infrastructure are rarely broken by routing instability. Of all vantage points (VPs), fewer than 0.15% VP’s TCP connections frequently break due to timeout in 5s during all 17 hours we observed. We only observe such frequent TCP connection break in 1 service out of all 12 anycast services studied. A second problem is DNS spoofing, where a third-party can intercept the DNS query and return a false answer. We examine DNS spoofing to study two aspects of security–integrity and privacy, and we design an algorithm to detect spoofing and distinguish different mechanisms to spoof anycast-based DNS. We show that DNS spoofing is uncommon, happening to only 1.7% of all VPs, although increasing over the years. Among all three ways to spoof DNS–injections, proxies, and third-party anycast site (prefix hijack), we show that third-party anycast site is the least popular one. Last, diagnosing poor latency and improving the latency can be difficult for CDNs. We develop a new approach, BAUP (bidirectional anycast unicast probing), which detects inefficient routing with better routing replacement provided. We use BAUP to study anycast latency. By applying BAUP and changing peering policies, a commercial CDN is able to significantly reduce latency, cutting median latency in half from 40ms to 16ms for regional users.

Lan defended her PhD when USC was on work-from-home due to COVID-19; she is the third ANT student with a fully on-line PhD defense.

Categories
Papers Publications

new journal paper “Plumb: Efficient Stream Processing of Multi-User Pipelines” in the Journal of Software: Practice and Experience

We have published a new journal paper “Plumb: Efficient Stream Processing of Multi-User Pipelines” in Wiley’s Journal of Software: Practice and Experience, available at https://onlinelibrary.wiley.com/doi/10.1002/spe.2909

Plumb provides a new pipeline-graph abstraction that allows multiple users to specify workflows in which Plumb can detect and elimiate duplicate processing and handle processing skew due to unbalanced data or stages. The end result is that users get their results faster and a shared cluster is efficiently utilized.

From the abstract of our journal paper:

Operational services run 24×7 and require analytics pipelines to evaluate performance. In mature services such as DNS, these pipelines often grow to many stages developed by multiple, loosely-coupled teams. Such pipelines pose two problems: first, computation and data storage may be duplicated across components developed by different groups, wasting resources. Second, processing can be skewed, with structural skew occurring when different pipeline stages need different amounts of resources, and computational skew occurring when a block of input data requires increased resources. Duplication and structural skew both decrease efficiency, increasing cost, latency, or both. Computational skew can cause pipeline failure or deadlock when resource consumption balloons; we have seen cases where pessimal traffic increases CPU requirements 6-fold. Detecting duplication is challenging when components from multiple teams evolve independently and require fault isolation. Skew management is hard due to dynamic workloads coupled with the conflicting goals of both minimizing latency and maximizing utilization. We propose Plumb, a framework to abstract stream processing as large-block streaming (LBS) for a multi-stage, multi-user workflow. Plumb users express analytics as a DAG of processing modules, allowing Plumb to integrate and optimize workflows from multiple users. Many real-world applications map to the LBS abstraction. Plumb detects and eliminates duplicate computation and storage, and it detects and addresses both structural and computational skew by tracking computation across the pipeline. We exercise Plumb using the analytics pipeline for B-Root DNS. We compare Plumb to a hand-tuned system, cutting latency to one-third the original, and requiring 39% fewer container hours, while supporting more flexible, multi-user analytics and providing greater robustness to DDoS-driven demands.

This journal paper is joint work of Abdul Qadeer and  John Heidemann from USC/ISI.

Plumb is open source software and we will be interested in beta testers. Please contact us if you think it would be useful to manage your workflows over one or a cluster of computers.

Categories
Internet Outages

Observing the CenturyLink outage on 2020-08-30

CenturyLink / Level3 was reported to have a major outage on Sunday, 2020-08-30 (as reported on CNN and discussed on slashdot).

This outage was very clear in our Trinocular near-real-time outage detection system. We have summarized the details with images, before, during, and after, and an animation of the nearly 7-hour event or see the event on our near-real-time outage website.

This outage is one of the largest U.S. nation-wide events since the 2014-08-27 Time Warner outage.

Categories
Presentations

Deep Dive into DNS at IETF108

The Domain Name System (DNS) is responsible for handling the initial steps of almost all connections on the Internet. USC/ISI’s Wes Hardaker, along with Geoff Houston and Joao Damas from APNIC, gave a “Deep Dive” presentation on how the DNS works at the 108th IETF conference. The recording is available on YouTube for those that missed it.