Data-Driven Intelligence for Characterizing Internet-Scale IoT Exploitations

摘要: While the security issue associated with Internet-of-Things (IoT) continues to attract significant attention from research and operational communities, visibility of IoT security-related data hinders prompt inference remediation maliciousness. In an effort address problem at large, in this work, we extend passive monitoring measurements by investigating network telescope infer analyze malicious activities generated compromised devices deployed various domains. Explicitly, develop a data-driven approach pinpoint exploited devices, investigate differentiate their illicit actions, examine hosting environments. More importantly, conduct discussions entities obtain IP allocation information, which further allows us attribute exploitations per business sector (i.e., education, financial, manufacturing, etc.). Our analysis draws upon 1.2 TB darknet that was collected /8 for 1 day period. The outcome signifies alarming number devices. Notably, around 940 them fell victims DDoS attacks, while 55,000 nodes were shown be compromised, aggressively probing Internet-wide hosts. Additionally, inferred critical sectors such as financial healthcare realms.