congratulations to Calvin Ardi for his new PhD

I would like to congratulate Dr. Calvin Ardi for defending his PhD in April 2020 and completing his doctoral dissertation “Improving Network Security through Collaborative Sharing” in June 2020.

From the abstract:

Calvin Ardi and John Heidemann (inset), after Calvin filed his PhD dissertation.

As our world continues to become more interconnected through the
Internet, cybersecurity incidents are correspondingly increasing in
number, severity, and complexity. The consequences of these attacks
include data loss, financial damages, and are steadily moving from the
digital to the physical world, impacting everything from public
infrastructure to our own homes. The existing mechanisms in
responding to cybersecurity incidents have three problems: they
promote a security monoculture, are too centralized, and are too slow.

In this thesis, we show that improving one’s network security strongly
benefits from a combination of personalized, local detection, coupled
with the controlled exchange of previously-private network information
with collaborators. We address the problem of a security monoculture
with personalized detection, introducing diversity by tailoring to the
individual’s browsing behavior, for example. We approach the problem
of too much centralization by localizing detection, emphasizing
detection techniques that can be used on the client device or local
network without reliance on external services. We counter slow
mechanisms by coupling controlled sharing of information with
collaborators to reactive techniques, enabling a more efficient
response to security events.

We prove that we can improve network security by demonstrating our
thesis with four studies and their respective research contributions
in malicious activity detection and cybersecurity data sharing. In
our first study, we develop Content Reuse Detection, an approach to
locally discover and detect duplication in large corpora and apply our
approach to improve network security by detecting “bad
neighborhoods” of suspicious activity on the web. Our second study
is AuntieTuna, an anti-phishing browser tool that implements personalized,
local detection of phish with user-personalization and improves
network security by reducing successful web phishing attacks. In our
third study, we develop Retro-Future, a framework for controlled information
exchange that enables organizations to control the risk-benefit
trade-off when sharing their previously-private data. Organizations
use Retro-Future to share data within and across collaborating organizations,
and improve their network security by using the shared data to
increase detection’s effectiveness in finding malicious activity.
Finally, we present AuntieTuna2.0 in our fourth study, extending the proactive
detection of phishing sites in AuntieTuna with data sharing between friends.
Users exchange previously-private information with collaborators to
collectively build a defense, improving their network security and
group’s collective immunity against phishing attacks.

Calvin defended his PhD when USC was on work-from-home due to COVID-19; he is the second ANT student with a fully on-line PhD defense.


congratulations to Hang Guo for his new PhD

I would like to congratulate Dr. Hang Guo for defending his PhD in April 2020 and completing his doctoral dissertation “Detecting and Characterizing Network Devices Using
Signatures of Traffic About End-Points” in May 2020.

Hang Guo and John Heidemann (inset), after Hang filed his PhD dissertation.

From the abstract:

The Internet has become an inseparable part of our society. Since the Internet is essentially a distributed system of billions of inter-connected, networked devices, learning about these devices is essential for better understanding, managing and securing the Internet. To study these network devices, without direct control over them or direct contact with their users, requires traffic-based methods for detecting devices. To identify target devices from traffic measurements, detection of network devices relies on signatures of traffic, mapping from certain characteristics of traffic to target devices. This dissertation focuses on device detection that use signatures of traffic about end-points: mapping from characteristics of traffic end-point, such as counts and identities, to target devices. The thesis of this dissertation is that new signatures of traffic about end-points enable detection and characterizations of new class of network devices. We support this thesis statement through three specific studies, each detecting and characterizing a new class of network devices with a new signature of traffic about end-points. In our first study, we present detection and characterization of network devices that rate limit ICMP traffic based on how they change the responsiveness of traffic end-points to active probings. In our second study, we demonstrate mapping identities of traffic end-points to a new class of network devices: Internet-of-Thing (IoT) devices. In our third study, we explore detecting compromised IoT devices by identifying IoT devices talking to suspicious end-points. Detection of these compromised IoT devices enables us to mitigate DDoS traffic between them and suspicious end-points.

Hang defend his PhD when USC was on work-from-home due to COVID-19, so he is the first ANT student with a fully on-line PhD defense.

Social Students

graduation lunch in honor of Liang Zhu

In October we had a ANT research group lunch to celebrate the PhD graduation of Liang Zhu.  Congratulations on his accomplishments and we all enjoyed tasty dim sum.

A going-away lunch for Liang Zhu (on the left), celebrating his PhD graduation, with the ANT lab and family members.
A going-away lunch for Liang Zhu (on the left), celebrating his PhD graduation, with the ANT lab and family members.

Announcements Students

congratulations to Liang Zhu for his new PhD

I would like to congratulate Dr. Liang Zhu for defending his PhD in August 2018 and completing his doctoral dissertation “Balancing Security and Performance of Network Request-Response Protocols” in September 2018.

Liang Zhu (left) and John Heidemann, after Liang’s PhD defense.

From the abstract:

The Internet has become a popular tool to acquire information and knowledge. Usually information retrieval on the Internet depends on request-response protocols, where clients and servers exchange data. Despite of their wide use, request-response protocols bring challenges for security and privacy. For example, source-address spoofing enables denial-of-service (DoS) attacks, and eavesdropping of unencrypted data leaks sensitive information in request-response protocols. There is often a trade-off between security and performance in request-response protocols. More advanced protocols, such as Transport Layer Security (TLS), are proposed to solve these problems of source spoofing and eavesdropping. However, developers often avoid adopting those advanced protocols, due to performance costs such as client latency and server memory requirement. We need to understand the trade-off between security and performance for request-response protocols and find a reasonable balance, instead of blindly prioritizing one of them.
This thesis of this dissertation states that it is possible to improve security of network request-response protocols without compromising performance, by protocol and deployment optimizations, that are demonstrated through measurements of protocol developments and deployments. We support the thesis statement through three specific studies, each of which uses measurements and experiments to evaluate the development and optimization of a request-response protocol. We show that security benefits can be achieved with modest performance costs. In the first study, we measure the latency of OCSP in TLS connections. We show that OCSP has low latency due to its wide use of CDN and caching, while identifying certificate revocation to secure TLS. In the second study, we propose to use TCP and TLS for DNS to solve a range of fundamental problems in DNS security and privacy. We show that DNS over TCP and TLS can achieve favorable performance with selective optimization. In the third study, we build a configurable, general-purpose DNS trace replay system that emulates global DNS hierarchy in a testbed and enables DNS experiments at scale efficiently. We use this system to further prove the reasonable performance of DNS over TCP and TLS at scale in the real world.

In addition to supporting our thesis, our studies have their own research contributions. Specifically, In the first work, we conducted new measurements of OCSP by examining network traffic of OCSP and showed a significant improvement of OCSP latency: a median latency of only 20ms, much less than the 291ms observed in prior work. We showed that CDN serves 94% of the OCSP traffic and OCSP use is ubiquitous. In the second work, we selected necessary protocol and implementation optimizations for DNS over TCP/TLS, and suggested how to run a production TCP/TLS DNS server [RFC7858]. We suggested appropriate connection timeouts for DNS operations: 20s at authoritative servers and 60s elsewhere. We showed that the cost of DNS over TCP/TLS can be modest. Our trace analysis showed that connection reuse can be frequent (60%-95% for stub and recursive resolvers). We showed that server memory is manageable (additional 3.6GB for a recursive server), and latency of connection-oriented DNS is acceptable (9%-22% slower than UDP). In the third work, we showed how to build a DNS experimentation framework that can scale to emulate a large DNS hierarchy and replay large traces. We used this experimentation framework to explore how traffic volume changes (increasing by 31%) when all DNS queries employ DNSSEC. Our DNS experimentation framework can benefit other studies on DNS performance evaluations.


congratulations to Xun Fan for his new PhD

I would like to congratulate Dr. Xun Fan for defending his PhD in May 2015 and completing his doctoral dissertation “Enabling Efficient Service Enumeration Through Smart Selection of Measurements” in July 2015.

Xun Fan (left) and John Heidemann, after Xun's PhD defense.
Xun Fan (left) and John Heidemann, after Xun’s PhD defense.

From the abstract:

The Internet is becoming more and more important in our daily lives. Both the government and industry invest in the growth of the Internet, bringing more users to the world of networks. As the Internet grows, researchers and operators need to track and understand the behavior of global Internet services to achieve smooth operation. Active measurements are often used to study behavior of large Internet service, and efficient service enumeration is required. For example, studies of Internet topology may need active probing to all visible network prefixes; monitoring large replicated service requires periodical enumeration of all service replicas. To achieve efficient service enumeration, it is important to select probing sources and destinations wisely. However, there are challenges for making smart selection of probing sources and destinations. Prior methods to select probing destinations are either inefficient or hard to maintain. Enumerating replicas of large Internet services often requires many widely distributed probing sources. Current measurement platforms don’t have enough probing sources to approach complete enumeration of large services.

This dissertation makes the thesis statement that smart selection of probing sources and destinations enables efficient enumeration of global Internet services to track and understand their behavior. We present three studies to demonstrate this thesis statement. First, we propose new automated approach to generate a list of destination IP addresses that enables efficient enumeration of Internet edge links. Second, we show that using large number of widely distributed open resolvers enables efficient enumeration of anycast nodes which helps study abnormal behavior of anycast DNS services. In our last study, we efficiently enumerate Front-End (FE) Clusters of Content Delivery Networks (CDNs) and use the efficient enumeration to track and understand the dynamics of user-to-FE Cluster mapping of large CDNs. We achieve the efficient enumeration of CDN FE Clusters by selecting probing sources from a large set of open resolvers. Our selected probing sources have smaller number of open resolvers but provide same coverage on CDN FE Cluster as the larger set.

In addition to our direct results, our work has also been used by several published studies to track and understand the behavior of Internet and large network services. These studies not only support our thesis as additional examples but also suggest this thesis can further benefit many other studies that need efficient service enumeration to track and understand behavior of global Internet services.


congratulations to Lin Quan for his new PhD

I would like to congratulate Dr. Lin Quan for defending his PhD in Dec. 2013 and his doctoral disseration “Learning about the Internet through Efficient Sampling and Aggregation” in Jan. 2014.

Lin Quan (left) and John Heidemann, after Lin's PhD defense.
Lin Quan (left) and John Heidemann, after Lin’s PhD defense.

From the abstract:

The Internet is important for nearly all aspects of our society, affecting ordinary people, businesses, and social activities. Because of its importance and wide-spread applications, we want to have good knowledge about Internet’s operation, reliability and performance, through various kinds of measurements. However, despite the wide usage, we only have limited knowledge of its overall performance and reliability. The first reason of this limited knowledge is that there is no central governance of the Internet, making both active and passive measurements hard. The second reason is the huge scale of the Internet. This makes brute-force analysis hard because of practical computing resource limits such as CPU, memory and probe rate.

This thesis states that sampling and aggregation are necessary to overcome resource constraints in time and space to learn about better knowledge of the Internet. Many other Internet measurement studies also utilize sampling and aggregation techniques to discover properties of the Internet. We distinguish our work by exploring novel mechanisms and new knowledge in several specific areas. First, we aggregate short-time-scale observations and use an efficient multi-time-scale query scheme to discover the properties and reasons of long-lived Internet flows. Second, we sample and probe /24 blocks in the IPv4 address space, and use greedy clustering algorithms to efficiently characterize Internet outages. Third, we show an efficient and effective aggregation technique by visualization and clustering. This technique makes both manual inspection and automated characterization easier. Last, we develop an adaptive probing system to study global scale Internet reliability. It samples and adapts probe rate within each /24 block for accurate beliefs. By aggregation and correlation to other domains, we are also able to study broader policy effects on Internet use, such as political causes, economic conditions, and access technologies.

This thesis provides several examples of Internet knowledge discovery with new mechanisms of sampling and aggregation techniques. We believe our approaches of new sampling and aggregation mechanisms can be used by and will inspire new ways for future Internet measurement systems to overcome resource constraints, such as large amount and dispersed data.



congratulations to Xue Cai for her new PhD

I would like to congratulate Dr. Xue Cai for defending her PhD and filing her doctoral disseration “Global Analysis and Modeling on Decentralized Internet” in Dec. 2013.

Xue Cai (left) and John Heidemann, after her PhD defense.
Xue Cai (left) and John Heidemann, after her PhD defense.

From the abstract:

Better understanding about Internet infrastructure is crucial to improve the reliability, performance, and security of web services. The need for this understanding then drives research in network measurements. Internet measurements explore a variety of data related to a specific topic and then develop approaches to transform data into useful understanding about the topic. This process is not straightforward since available data often only contains indirect information that may appear to have limited connection to the topic.
This body of work asserts that systematic approaches can overcome data limitations to improve understanding about important aspects of the Internet infrastructure. We demonstrate the validity of our thesis statement by providing three specific examples that develop novel approaches and provide novel understanding compared to prior work. In particular, we employ four systematic approaches—statistical, clustering, modeling, and what-if approach—to understand three important aspects of the Internet: the efficiency and management of IPv4 addresses, the ownership of Autonomous Systems (ASes), and the robustness of web services when facing critical facility disruption. These approaches have addressed a variety of challenges posed by indirect, incomplete, over-fit, noisy and unknown data; they in turn enable us to improve understanding about the Internet.
Each of our three studies explores a different area of the problem space and opens a much larger area of opportunity. The data limitations addressed by our approaches also occur in many other problems. We believe our approaches can inspire future work to solve these problems and in turn provide more useful understanding about the Internet.