Data-Driven Intelligence for Characterizing Internet-scale IoT Exploitations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F18%3A00108865" target="_blank" >RIV/00216224:14610/18:00108865 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8644468" target="_blank" >https://ieeexplore.ieee.org/document/8644468</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/GLOCOMW.2018.8644468" target="_blank" >10.1109/GLOCOMW.2018.8644468</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-Driven Intelligence for Characterizing Internet-scale IoT Exploitations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Data-Driven Intelligence for Characterizing Internet-scale IoT Exploitations

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2018 IEEE Globecom Workshops

ISBN

9781538649206

ISSN

2166-0069

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE

Místo vydání

Abu Dhabi

Místo konání akce

Abu Dhabi

Datum konání akce

9. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000462817000273