Unsolicited traffic sent to advertised network space that does not host active services provides insights about misconfigurations as well as potentially malicious activities, including the spread of Botnets, DDoS campaigns, and exploitation of vulnerabilities. Network telescopes have been used for many years to monitor such unsolicited traffic. Unfortunately, they are limi the available address space for such tasks and, thus, limited to specific geographic and/or network regions.

In this paper, we introduce a novel concept to broadly capture unsolicited Internet traffic, which we call a "meta-telescope". A meta-telescope is based on the intuition that, with the availability of appropriate vantage points, one can (i) infer which address blocks on the Internet are unused and (ii) capture traffic towards them-both without having control of such address blocks. From this intuition, we develop and evaluate a methodology for identifying unlikely to be used Internet address space and build a meta-telescope that has very desirable properties, such as broad coverage of dark space both in terms of size and topological placement. Such meta-telescope identifies and captures unsolicited traffic to more than 350k /24 blocks in more than 7k ASes. Through the analysis of background radiation towards these networks, we also highlight that unsolicited traffic differs by destination network/geographic region as well as by network type. Finally, we discuss our experience and challenges when operating a meta-telescope in the wild.@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.

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In March 2020, the World Health Organization declared the Corona Virus 2019 (COVID-19) outbreak a global pandemic. As a result, billions of people were either encouraged or forced by their governments to stay home to reduce the spread of the virus. This caused many to turn to the Internet for work, education, social interaction, and entertainment. With the Internet demand rising at an unprecedented rate, the question of whether the Internet could sustain this additional load emerged. To answer this question, this paper will review the impact of the first year of the COVID-19 pandemic on Internet traffic in order to analyze its performance. In order to keep our study broad, we collect and analyze Internet traffic data from multiple locations at the core and edge of the Internet. From this, we characterize how traffic and application demands change, to describe the "new normal,"and explain how the Internet reacted during these unprecedented times.

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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.

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