Simone Cossaro (University of Trieste), Damiano Ravalico (University of Trieste), Rodolfo Vieira Valentim (University of Turin), Martino Trevisan (University of Trieste), Idilio Drago (University of Turin)

Network telescopes (IP addresses hosting no services) are valuable for observing unsolicited Internet traffic from scanners, crawlers, botnets, and misconfigured hosts. This traffic is known as Internet radiation, and its monitoring with telescopes helps in identifying malicious activities. Yet, the deployment of telescopes is expensive. Meanwhile, numerous public blocklists aggregate data from various sources to track IP addresses involved in malicious activity. This raises the question of whether public blocklists already provide sufficient coverage of these actors, thus rendering new network telescopes unnecessary. We address this question by analyzing traffic from four geographically distributed telescopes and dozens of public blocklists over a two-month period. Our findings show that public blocklists include approximately 71% of IP addresses observed in the telescopes. Moreover, telescopes typically observe scanning activities days before they appear in blocklists. We also find that only 4 out of 50 lists contribute the majority of the coverage, while the addresses evading blocklists present more sporadic activity. Our results demonstrate that distributed telescopes remain valuable assets for network security, providing early detection of threats and complementary coverage to public blocklists. These results call for more coordination among telescope operators and blocklist providers to enhance the defense against emerging threats.

View More Papers

Do We Really Need to Design New Byzantine-robust Aggregation...

Minghong Fang (University of Louisville), Seyedsina Nabavirazavi (Florida International University), Zhuqing Liu (University of North Texas), Wei Sun (Wichita State University), Sundararaja Iyengar (Florida International University), Haibo Yang (Rochester Institute of Technology)

Read More

Poster: Understanding User Acceptance of Privacy Labels: Barriers and...

Jingwen Yan (Clemson University), Mohammed Aldeen (Clemson University), Jalil Harris (Clemson University), Kellen Grossenbacher (Clemson University), Aurore Munyaneza (Texas Tech University), Song Liao (Texas Tech University), Long Cheng (Clemson University)

Read More

Try to Poison My Deep Learning Data? Nowhere to...

Yansong Gao (The University of Western Australia), Huaibing Peng (Nanjing University of Science and Technology), Hua Ma (CSIRO's Data61), Zhi Zhang (The University of Western Australia), Shuo Wang (Shanghai Jiao Tong University), Rayne Holland (CSIRO's Data61), Anmin Fu (Nanjing University of Science and Technology), Minhui Xue (CSIRO's Data61), Derek Abbott (The University of Adelaide, Australia)

Read More