Research approach

To tackle the challenges the current development of the Internet poses, our research approach is to collect data from operational networks, analyze them using big-data analytics to identify invariants, revisit assumptions, and detect where current performance bottlenecks in the Internet architecture are. Hereby, we use simulation environments to validate our analysis results and to enable “what-if” studies. These insights are then  the foundation for shaping the future Internet via optimizations and alternative designs. Hereby, we consider protocol enhancements, novel network management tools, and how to take advantage of concepts from software-defined networking.

The research group will tackle the following topics:

Internet Measurement: Based on continuous observations of the Internet the goal of this group is to understand how the Internet evolves. But it also involves understanding the interdependencies between the network elements and the applications as well as the impact of failures.

Future Internet: Here the focus is on how to evolve the Internet to overcome its current limitations, e.g., by adding better network management capabilities and enhancing the capabilities of CloudNets.

Wide area data analytics: The challenge here is to enable wide area data analytics via collaborative data processing pipelines.

Research areas

Emerging Platforms/Communities on the Web

Coordinator: Dr. Savvas Zannettou

The Web consists of numerous Web communities, news sources, and services, which are often used by various actors for potentially nefarious purposes. At the same time, it has become increasingly easier to create new platforms and communities, making the Web become a bigger and more complex ecosystem. We aim to study emerging platforms and Web communities that can have a large impact and influence both on the online and offline world and contribute to the research community by making publicly available datasets from social networks.

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Flow queries

Coordinator: Said Jawad Saidi

Many network operations, ranging from attack investigation and mitigation to traffic management, require answering network-wide flow queries in seconds. Although flow records are collected at each router, using available traffic capture utilities, querying the resulting datasets from hundreds of routers across sites and over time remains a significant challenge due to the sheer traffic volume and distributed nature of flow records. In this project, we investigate how to improve the response time for apriori unknown network-widequeries.

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Network and Cloud Systems

Coordinator: Dr. Yiting Xia

Computer networks have become the “new electricity” to facilitate rich cloud services. We are generally interested in building network infrastructure for cloud computing. Our research follows a cross-layer approach and covers broad topics for optimizing the network stack, including switch hardware, network protocols, software systems, and cloud applications.

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Routing and Traffic Engineering

Coordinator: Lars Prehn

Availability, accessibility, and reactivity are the cornerstones of modern web applications. Those characteristics require the underlying network infrastructure to operate not only extremely efficient but also highly robust and flexible. We intensively study the internal and external (routing-related) techniques that networks use to achieve those goals and use our insights to shape the traffic delivery of the future.

Online Content Moderation

Coordinator: Dr. Savvas Zannettou

Online content moderation is an important aspect of online social networks as it ensures that content posted by users abides by the platform’s guidelines and is appropriate for other users. due to the complexity and black-box nature of content moderation there are several open research avenues, such as the effectiveness of specific moderation strategies or how to maximize the output of content moderation and minimize the mental toll on human moderators from continuous exposure to harmful content.

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Online Hate Speech

Coordinator: Dr. Savvas Zannettou

The spread of offensive language and hate speech online is an important and timely issue that exists on social networks. The continuous exposure to hateful content can have catastrophic consequences as it can lead to user radicalization and real-world violence (e.g., hate attacks against specific demographic groups). Despite the problem’s importance and societal implications, there are several research gaps in understanding, detecting, and mitigating the spread of hate speech. In this line of work, we aim to study and understand specific forms of hate speech (e.g., Anti Semitism, anti-chinese sentiments, etc.) and develop techniques to automatically detect instances of hate speech.

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Understanding, Detecting, and Mitigating Weaponized Information

Coordinator: Dr. Savvas Zannettou

Weaponized information refers to carefully crafted information that aims to deceive people or information that is presented in such a way to manipulate or attack users. The dissemination of weaponized information at large scale can have devastating effects on the online and offline world. Motivated by the importance and impact that weaponized information can have to society, in this line of research, we aim to understand, design techniques to automatically detect instances of weaponized information, and investigate possible mitigation strategies.

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