Category: Publications

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Publications Technical Report

new technical report “Detecting IoT Devices in the Internet (Extended)”

We have released a new technical report “Detecting IoT Devices in the Internet (Extended)” as ISI-TR-726.

ISP-Level Deployment for  26 IoT Device Types. From Figure 2 of [Guo18c].
From the abstract of our technical report:

Distributed Denial-of-Service (DDoS) attacks launched from compromised Internet-of-Things (IoT) devices have shown how vulnerable the Internet is to large-scale DDoS attacks. To understand the risks of these attacks requires learning about these IoT devices: where are they? how many are there? how are they changing? This paper describes three new methods to find IoT devices on the Internet: server IP addresses in traffic, server names in DNS queries, and manufacturer information in TLS certificates. Our primary methods (IP addresses and DNS names) use knowledge of servers run by the manufacturers of these devices. We have developed these approaches with 10 device models from 7 vendors. Our third method uses TLS certificates obtained by active scanning. We have applied our algorithms to a number of observations. Our IP-based algorithms see at least 35 IoT devices on a college campus, and 122 IoT devices in customers of a regional IXP. We apply our DNSbased algorithm to traffic from 5 root DNS servers from 2013 to 2018, finding huge growth (about 7×) in ISPlevel deployment of 26 device types. DNS also shows similar growth in IoT deployment in residential households from 2013 to 2017. Our certificate-based algorithm finds 254k IP cameras and network video recorders from 199 countries around the world.

We make operational traffic we captured from 10 IoT devices we own public at https://ant.isi.edu/datasets/iot/. We also use operational traffic of 21 IoT devices shared by University of New South Wales at http://149.171.189.1/.

This technical report is joint work of Hang Guo and  John Heidemann from USC/ISI.

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Papers Publications

New workshop paper “IP-Based IoT Device Detection”

We have published a new paper “IP-Based IoT Device Detection” in the Second ACM Workshop on Internet-of-Things Security and Privacy (IoTS&P 2018) in Budapest, Hungary, co-located with SIGCOMM 2018.

IoT devices we detect in use at a campus (Table 3 from [Guo18b])
From the abstract of our  paper:

Recent IoT-based DDoS attacks have exposed how vulnerable the Internet can be to millions of insufficiently secured IoT devices. To understand the risks of these attacks requires
learning about these IoT devices—where are they, how many are there, how are they changing? In this paper, we propose
a new method to find IoT devices in Internet to begin to assess this threat. Our approach requires observations of flow-level network traffic and knowledge of servers run by
the manufacturers of the IoT devices. We have developed our approach with 10 device models by 7 vendors and controlled
experiments. We apply our algorithm to observations from 6 days of Internet traffic at a college campus and partial traffic
from an IXP to detect IoT devices.

We make operational traffic we captured from 10 IoT devices we own public at https://ant.isi.edu/datasets/iot/. We also use operational traffic of 21 IoT devices shared by University of New South Wales at http://149.171.189.1/.

This paper is joint work of Hang Guo and  John Heidemann from USC/ISI.

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Publications Technical Report

new technical report “When the Dike Breaks: Dissecting DNS Defenses During DDoS (extended)”

We released a new technical report “When the Dike Breaks: Dissecting DNS Defenses During DDoS (extended)”, ISI-TR-725, available at https://www.isi.edu/~johnh/PAPERS/Moura18a.pdf.

Moura18a Figure 6a, Answers received during a DDoS attack causing 100% packet loss with pre-loaded caches.

From the abstract:

The Internet’s Domain Name System (DNS) is a frequent target of Distributed Denial-of-Service (DDoS) attacks, but such attacks have had very different outcomes—some attacks have disabled major public websites, while the external effects of other attacks have been minimal. While on one hand the DNS protocol is a relatively simple, the system has many moving parts, with multiple levels of caching and retries and replicated servers. This paper uses controlled experiments to examine how these mechanisms affect DNS resilience and latency, exploring both the client side’s DNS user experience, and server-side traffic. We find that, for about about 30% of clients, caching is not effective. However, when caches are full they allow about half of clients to ride out server outages, and caching and retries allow up to half of the clients to tolerate DDoS attacks that result in 90% query loss, and almost all clients to tolerate attacks resulting in 50% packet loss. The cost of such attacks to clients are greater median latency. For servers, retries during DDoS attacks increase normal traffic up to 8x. Our findings about caching and retries can explain why some real-world DDoS cause service outages for users while other large attacks have minimal visible effects.

Datasets from this paper are available at no cost and are listed at https://ant.isi.edu/datasets/dns/#Moura18a_data.

 

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Papers Publications

new conference paper “Detecting ICMP Rate Limiting in the Internet” in PAM 2018

We have published a new conference “Detecting ICMP Rate Limiting in the Internet” in PAM 2018 (the Passive and Active Measurement Conference) in Berlin, Germany.

Figure 4 from [Guo18a] Confirming a block is rate limited with additional probing
Figure 4 from [Guo18a] confirming a bock is rate limited, comparing experimental results with models of rate-limited and non-rate-limited behavior.
From the abstract of our conference paper:

Comparing model and experimental effects of rate limiting (Figure 4 from [Guo18a] )
ICMP active probing is the center of many network measurements. Rate limiting to ICMP traffic, if undetected, could distort measurements and create false conclusions. To settle this concern, we look systematically for ICMP rate limiting in the Internet. We create FADER, a new algorithm that can identify rate limiting from user-side traces with minimal new measurement traffic. We validate the accuracy of FADER with many different network configurations in testbed experiments and show that it almost always detects rate limiting. With this confidence, we apply our algorithm to a random sample of the whole Internet, showing that rate limiting exists but that for slow probing rates, rate-limiting is very rare. For our random sample of 40,493 /24 blocks (about 2% of the responsive space), we confirm 6 blocks (0.02%!) see rate limiting at 0.39 packets/s per block. We look at higher rates in public datasets and suggest that fall-off in responses as rates approach 1 packet/s per /24 block is consistent with rate limiting. We also show that even very slow probing (0.0001 packet/s) can encounter rate limiting of NACKs that are concentrated at a single router near the prober.

Datasets we used in this paper are all public. ISI Internet Census and Survey data (including it71w, it70w, it56j, it57j and it58j census and survey) are available at https://ant.isi.edu/datasets/index.html. ZMap 50-second experiments data are from their WOOT 14 paper and can be obtained from ZMap authors upon request.

This conference report is joint work of Hang Guo and  John Heidemann from USC/ISI.

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DNS Papers Presentations Publications

New paper and talk “Enumerating Privacy Leaks in DNS Data Collected above the Recursive” at NDSS DNS Privacy Workshop 2018

Basileal Imana presented the paper “Enumerating Privacy Leaks in DNS Data Collected  above the Recursive” at NDSS DNS Privacy Workshop in San Diego, California, USA on February 18, 2018. Talk slides are available at https://ant.isi.edu/~imana/presentations/Imana18b.pdf and paper is available at  https://ant.isi.edu/~imana/papers/Imana18a.pdf, or can be found at the DNS privacy workshop page.

From the abstract:

Threat model for enumerating leaks above the recursive (left). Percentage of four categories of queries containing IPv4 addresses in their QNAMEs. (right)

As with any information system consisting of data derived from people’s actions, DNS data is vulnerable to privacy risks. In DNS, users make queries through recursive resolvers to authoritative servers. Data collected below (or in) the recursive resolver directly exposes users, so most prior DNS data sharing focuses on queries above the recursive resolver. Data collected above a recursive resolver has largely been seen as posing a minimal privacy risk since recursive resolvers typically aggregate traffic for many users, thereby hiding their identity and mixing their traffic. Although this assumption is widely made, to our knowledge it has not been verified. In this paper we re-examine this assumption for DNS traffic above the recursive resolver. First, we show that two kinds of information appear in query names above the recursive resolver: IP addresses and sensitive domain names, such as those pertaining to health, politics, or personal or lifestyle information. Second, we examine how often these classes of potentially sensitive names appear in Root DNS traffic, using 48 hours of B-Root data from April 2017.

This is a joint work by Basileal Imana (USC), Aleksandra Korolova (USC) and John Heidemann (USC/ISI).

The DITL dataset (ITL_B_Root-20170411) used in this work is available from DHS IMPACT, the ANT project, and through DNS-OARC.

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Publications Technical Report

new technical report “Back Out: End-to-end Inference of Common Points-of-Failure in the Internet (extended)”

We released a new technical report “Back Out: End-to-end Inference of Common Points-of-Failure in the Internet (extended)”, ISI-TR-724, available at https://www.isi.edu/~johnh/PAPERS/Heidemann18b.pdf.

From the abstract:

Clustering (from our event clustering algorithm) of 2014q3 outages from 172/8, showing 7 weeks including the 2014-08-27 Time Warner outage.

Internet reliability has many potential weaknesses: fiber rights-of-way at the physical layer, exchange-point congestion from DDOS at the network layer, settlement disputes between organizations at the financial layer, and government intervention the political layer. This paper shows that we can discover common points-of-failure at any of these layers by observing correlated failures. We use end-to-end observations from data-plane-level connectivity of edge hosts in the Internet. We identify correlations in connectivity: networks that usually fail and recover at the same time suggest common point-of-failure. We define two new algorithms to meet these goals. First, we define a computationally-efficient algorithm to create a linear ordering of blocks to make correlated failures apparent to a human analyst. Second, we develop an event-based clustering algorithm that directly networks with correlated failures, suggesting common points-of-failure. Our algorithms scale to real-world datasets of millions of networks and observations: linear ordering is O(n log n) time and event-based clustering parallelizes with Map/Reduce. We demonstrate them on three months of outages for 4 million /24 network prefixes, showing high recall (0.83 to 0.98) and precision (0.72 to 1.0) for blocks that respond. We also show that our algorithms generalize to identify correlations in anycast catchments and routing.

Datasets from this paper are available at no cost and are listed at https://ant.isi.edu/datasets/outage/, and we expect to release the software for this paper in the coming months (contact us if you are interested).

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Publications Software releases Technical Report

new technical report “LDplayer: DNS Experimentation at Scale”

We released a new technical report “LDplayer: DNS Experimentation at Scale”, ISI-TR-722, available at https://www.isi.edu/publications/trpublic/pdfs/ISI-TR-722.pdf.

ldplayer_overviewFrom the abstract:

DNS has evolved over the last 20 years, improving in security and privacy and broadening the kinds of applications it supports. However, this evolution has been slowed by the large installed base with a wide range of implementations that are slow to change. Changes need to be carefully planned, and their impact is difficult to model due to DNS optimizations, caching, and distributed operation. We suggest that experimentation at scale is needed to evaluate changes and speed DNS evolution. This paper presents LDplayer, a configurable, general-purpose DNS testbed that enables DNS experiments to scale in several dimensions: many zones, multiple levels of DNS hierarchy, high query rates, and diverse query sources. LDplayer provides high fidelity experiments while meeting these requirements through its distributed DNS query replay system, methods to rebuild the relevant DNS hierarchy from traces, and efficient emulation of this hierarchy of limited hardware. We show that a single DNS server can correctly emulate multiple independent levels of the DNS hierarchy while providing correct responses as if they were independent. We validate that our system can replay a DNS root traffic with tiny error (+/- 8ms quartiles in query timing and +/- 0.1% difference in query rate). We show that our system can replay queries at 87k queries/s, more than twice of a normal DNS Root traffic rate, maxing out one CPU core used by our customized DNS traffic generator. LDplayer’s trace replay has the unique ability to evaluate important design questions with confidence that we capture the interplay of caching, timeouts, and resource constraints. As an example, we can demonstrate the memory requirements of a DNS root server with all traffic running over TCP, and we identified performance discontinuities in latency as a function of client RTT.

Software developed in this paper is available at https://ant.isi.edu/software/ldplayer/.

 

 

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Papers Publications

new conference paper “A Look at Router Geolocation in Public and Commercial Databases” in IMC 2017

The paper “A Look at Router Geolocation in Public and Commercial Databases” has appeared in the 2017 Internet Measurement Conference (IMC) on November 1-3, 2017 in London, United Kingdom.

From the abstract:

Regional breakdown of the geolocation error for the geolocation databases vs. ground truth data.

Internet measurement research frequently needs to map infrastructure components, such as routers, to their physical locations. Although public and commercial geolocation services are often used for this purpose, their accuracy when applied to network infrastructure has not been sufficiently assessed. Prior work focused on evaluating the overall accuracy of geolocation databases, which is dominated by their performance on end-user IP addresses. In this work, we evaluate the reliability of router geolocation in databases. We use a dataset of about 1.64M router interface IP addresses extracted from the CAIDA Ark dataset to examine the country- and city-level coverage and consistency of popular public and commercial geolocation databases. We also create and provide a ground-truth dataset of 16,586 router interface IP addresses and their city-level locations, and use it to evaluate the databases’ accuracy with a regional breakdown analysis. Our results show that the databases are not reliable for geolocating routers and that there is room to improve their country- and city-level accuracy. Based on our results, we present a set of recommendations to researchers concerning the use of geolocation databases to geolocate routers.

The work in this paper was joint work by Manaf Gharaibeh, Anant Shah, Han Zhang, Christos Papadopoulos (Colorado State University), Brad Huffaker (CAIDA / UC San Diego), and Roya Ensafi (University of Michigan). The findings of this work are highlighted in an APNIC blog post “Should we trust the geolocation databases to geolocate routers?”. The ground truth datasets used in the paper are available via IMPACT.

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DNS Papers Publications

new journal paper “Detecting Malicious Activity With DNS Backscatter Over Time” in IEEE/ACM ToN Oct, 2017

The paper “Detecting Malicious Activity With DNS Backscatter Over Time ” appears in EEE/ACM  Transactions on Networking ( Volume: 25, Issue: 5, Oct. 2017 ).

From the abstract:

Network-wide activity is when one computer (the originator) touches many others (the targets). Motives for activity may be benign (mailing lists, CDNs, and research scanning), malicious (spammers and scanners for security vulnerabilities), or perhaps indeterminate (ad trackers). Knowledge of malicious activity may help anticipate attacks, and understanding benign activity may set a baseline or characterize growth. This paper identifies DNS backscatter as a new source of information about network-wide activity. Backscatter is the reverse DNS queries caused when targets or middleboxes automatically look up the domain name of the originator. Queries are visible to the authoritative DNS servers that handle reverse DNS. While the fraction of backscatter they see depends on the server’s location in the DNS hierarchy, we show that activity that touches many targets appear even in sampled observations. We use information about the queriers to classify originator activity using machine learning. Our algorithm has reasonable accuracy and precision (70–80%) as shown by data from three different organizations operating DNS servers at the root or country-level. Using this technique we examine nine months of activity from one authority to identify trends in scanning, identifying bursts corresponding to Heartbleed and broad and continuous scanning of ssh.

This paper furthers our understanding of evolution of malicious network activities from an earlier work that:
(1) Why our machine-learning based classifier (that relies on manually collected labeled data) does not port across physical sites and over time.
(2) Secondly paper recommends how to sustain good learning score over time and provides expected life-time of labeled data.

An excerpt from section III-E (Training Over Time):

Classification (§ III-D) is based on training, yet training accuracy is affected by the evolution of activity—specific examples come and go, and the behavior in each class evolves. Change happens for all classes, but the problem is particularly acute for malicious classes (such as spam) where the adversarial nature of the action forces rapid evolution (see § V).

 

Some datasets used in this paper can be found here:

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Papers Publications

new conference paper “Recursives in the Wild: Engineering Authoritative DNS Servers” in IMC 2017

The paper “Recursives in the Wild: Engineering Authoritative DNS Servers” will appear in the 2017 Internet Measurement Conference (IMC) on November 1-3, 2017 in London, United Kingdom.

Recursive DNS server selection of authoritatives, per continent. (Figure 4 from [Mueller17b].)
From the abstract:

In In Internet Domain Name System (DNS), services operate authoritative name servers that individuals query through recursive resolvers. Operators strive to provide reliability by operating multiple name servers (NS), each on a separate IP address, and by using IP anycast to allow NSes to provide service from many physical locations. To meet their goals of minimizing latency and balancing load across NSes and anycast, operators need to know how recursive resolvers select an NS, and how that interacts with their NS deployments. Prior work has shown some recursives search for low latency, while others pick an NS at random or round robin, but did not examine how prevalent each choice was. This paper provides the first analysis of how recursives select between name servers in the wild, and from that we provide guidance to operators how to engineer their name servers to reach their goals. We conclude that all NSes need to be equally strong and therefore we recommend to deploy IP anycast at every single authoritative.

All datasets used in this paper (but one) are available at https://ant.isi.edu/datasets/dns/index.html#recursives .