Amplification Distributed Denial of Service (DDoS) attacks' traffic and harm are at an all-time high. To defend against such attacks, distributed attack mitigation platforms, such as traffic scrubbing centers that operate in peering locations, e.g., Internet Exchange Points (IXP), have been deployed in the Internet over the years. These attack mitigation platforms apply sophisticated techniques to detect attacks and drop attack traffic locally, thus, act as sensors of attacks. However, it has not yet been systematically evaluated and reported to what extent coordination of these views by different platforms can lead to more effective mitigation of amplification DDoS attacks. In this paper, we ask the question: "Is it possible to mitigate more amplification attacks and drop more attack traffic when distributed attack mitigation platforms collaborate?"To answer this question, we collaborate with eleven IXPs that operate in three different regions. These IXPs have more than 2,120 network members that exchange traffic at the rate of more than 11 Terabits per second. We collect network data over six months and analyze more than 120k amplification DDoS attacks. To our surprise, more than 80% of the amplification DDoS are not detected locally, although the majority of the attacks are visible by at least three IXPs. A closer investigation points to the shortcomings, such as the multi-protocol profile of modern amplification attacks, the duration of the attacks, and the difficulty of setting appropriate local attack traffic thresholds that will trigger mitigation. To overcome these limitations, we design and evaluate a collaborative architecture that allows participant mitigation platforms to exchange information about ongoing amplification attacks. Our evaluation shows that it is possible to collaboratively detect and mitigate the majority of attacks with limited exchange of information and drop as much as 90% more attack traffic locally.@en

With the increasing cloud usage for access to fast and well-connected computational power, cloud outages have also become a growing risk for businesses and individuals alike. We derive a method to analyze publicly available BGP data to measure the visibility of cloud providers' outages on the Internet control plane. We then utilize this method to analyze an outage of Cloudflare, a large DNS and content provider. Cloudflare's outage study shows that visible traces can be found in BGP, enabling data-driven outage studies. @en

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.@en

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.@en

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.@en

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.@en

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.@en

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.@en

In this paper, we report on a measurement study by researchers from several institutions that collected and analyzed network data to assess the impact of the first wave of COVID-19 (February-June 2020) on the Internet traffic. The datasets from Internet Service Providers, Internet Exchange Points, and academic networks, primarily in Europe, provide a unique view on the changes of Internet traffic due to pandemic and the lockdown that forced hundreds of millions of citizens to stay and work from home. The analysis shows that the increase of Internet traffic was about 15-20 % within a couple of weeks, an increase that is typically spread over multiple months under typical operation. However, traffic during peak hours does not increase by more than 5 %. The increase was noticeably higher for specific applications, e.g., remote work applications, teleconferencing, video on demand; in some cases up to 200 %. However, overall, the Internet reacted well to these unprecedented times.@en