Internet-scale Insecurity of Consumer Internet of Things
Authors:
Open Access
- 12 October 2020
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Management Information Systems
- Vol. 11 (4) , 1-24
Abstract
The number of Internet-of-Things (IoT) devices actively communicating across the Internet is continually increasing, as these devices are deployed across a variety of sectors, constantly transferring private data across the Internet. Due to the extensive deployment of such devices, the continuous discovery and persistence of IoT-centric vulnerabilities in protocols, applications, hardware, and the improper management of such IoT devices has resulted in the rampant, uncontrolled spread of malware threatening consumer IoT devices. To this end, this work adopts a novel, macroscopic methodology for fingerprinting Internet-scale compromised IoT devices, revealing crucial cyber threat intelligence on the insecurity of consumer IoT devices. By developing data-driven techniques rooted in machine learning methods and analyzing 3.6 TB of network traffic data, we discover 855,916 compromised IP addresses, with 310,164 fingerprinted as IoT. Further analysis reveals China and Brazil to be hosting the most significant population of compromised IoT devices (100,000 and 55,000, respectively). Additionally, we provide a longitudinal analysis on data from one year ago against this work, revealing the evolving trends of IoT exploitation, such as the increased number of vendors targeted by malware, rising from 50 to 131. Moreover, countries such as China (420% increased infected IoT count) and Indonesia (177% increased infected IoT count) have seen notably high increases in infection rates. Last, we compare our geographic results against Global Cybersecurity Index (GCI) ratings, verifying that countries with high GCI ratings, such as the Netherlands and Germany, had relatively low infection rates. However, upon further inspection, we find that the GCI rate does not accurately represent the consumer IoT market, with countries such as China and Russia being rated with “high” CGI scores, yet hosting a large population of infected consumer IoT devices.Keywords
Funding Information
- National Science Foundation - Office of Advanced Cyberinfrastructure (#1907821)
This publication has 36 references indexed in Scilit:
- IoT Privacy and Security Challenges for Smart Home EnvironmentsInformation, 2016
- Smart Locks2016
- A probabilistic model to preprocess darknet data for cyber threat intelligence generation2016
- Extended Functionality Attacks on IoT Devices: The Case of Smart Lights2016
- Security analysis on consumer and industrial IoT devices2016
- A novel cyber security capability: Inferring Internet-scale infections by correlating malware and probing activitiesComputer Networks, 2016
- Darknet as a Source of Cyber Intelligence: Survey, Taxonomy, and CharacterizationIEEE Communications Surveys & Tutorials, 2015
- Security and privacy challenges in industrial internet of things2015
- Random ForestsMachine Learning, 2001
- Support-vector networksMachine Learning, 1995