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@inproceedings{1434137,
author = {Neshenko, Nataliia and Husák, Martin and BouandHarb, Elias and Čeleda, Pavel and AlandMulla, Sameera and Fachkha, Claude},
address = {Abu Dhabi},
booktitle = {2018 IEEE Globecom Workshops},
doi = {http://dx.doi.org/10.1109/GLOCOMW.2018.8644468},
keywords = {network monitoring;darknet;IoT;cyber security},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Abu Dhabi},
isbn = {978-1-5386-4920-6},
pages = {1-7},
publisher = {IEEE},
title = {Data-Driven Intelligence for Characterizing Internet-scale IoT Exploitations},
url = {https://ieeexplore.ieee.org/document/8644468},
year = {2018}
}
TY - JOUR
ID - 1434137
AU - Neshenko, Nataliia - Husák, Martin - Bou-Harb, Elias - Čeleda, Pavel - Al-Mulla, Sameera - Fachkha, Claude
PY - 2018
TI - Data-Driven Intelligence for Characterizing Internet-scale IoT Exploitations
PB - IEEE
CY - Abu Dhabi
SN - 9781538649206
KW - network monitoring;darknet;IoT;cyber security
UR - https://ieeexplore.ieee.org/document/8644468
L2 - https://ieeexplore.ieee.org/document/8644468
N2 - While the security issue associated with the Internet-of-Things (IoT) continues to attract significant attention from the research and operational communities, the visibility of IoT security-related data hinders the prompt inference and remediation of IoT maliciousness. In an effort to address the IoT security problem at large, in this work, we extend passive monitoring and measurements by investigating network telescope data to infer and analyze malicious activities generated by compromised IoT devices deployed in various domains. Explicitly, we develop a data-driven approach to pinpoint exploited IoT devices, investigate and differentiate their illicit actions, and examine their hosting environments. More importantly, we conduct discussions with various entities to obtain IP allocation information, which further allows us to attribute IoT exploitations per business sector (i.e., education, financial, manufacturing, etc.). Our analysis draws upon 1.2 TB of darknet data that was collected from a /8 network telescope for a 1 day period. The outcome signifies an alarming number of compromised IoT devices. Notably, around 940 of them fell victims of DDoS attacks, while 55,000 IoT nodes were shown to be compromised, aggressively probing Internet-wide hosts. Additionally, we inferred alarming IoT exploitations in various critical sectors such as the manufacturing, financial and healthcare realms.
ER -
NESHENKO, Nataliia, Martin HUSÁK, Elias BOU-HARB, Pavel ČELEDA, Sameera AL-MULLA a Claude FACHKHA. Data-Driven Intelligence for Characterizing Internet-scale IoT Exploitations. Online. In \textit{2018 IEEE Globecom Workshops}. Abu Dhabi: IEEE, 2018, s.~1-7. ISBN~978-1-5386-4920-6. Dostupné z: https://dx.doi.org/10.1109/GLOCOMW.2018.8644468.
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