Accessible from the open web: a qualitative analysis of the available open-source information involving cyber security and critical infrastructure. Issue 1 (26th April 2022)

• Record Type: Journal Article
• Authors: Zhang, Yuxuan (Cicilia); Frank, Richard; Warkentin, Noelle; Zakimi, Naomi
• Is Part Of: Journal of cybersecurity. Volume 8:Issue 1(2022)
• Publication Date: 2022-04-26
• Subjects: open-source intelligence -- critical Infrastructure -- cybersecurity -- cyber-attack -- industrial systems -- qualitative research; Computer security -- Periodicals; Computer networks -- Security measures -- Periodicals; 005.805
• DOI: 10.1093/cybsec/tyac003
• Languages: English
• ISSNs: 2057-2093

Abstract

Abstract: In order to efficiently manage and operate industrial-level production, an increasing number of industrial devices and critical infrastructure (CI) are now connected to the internet, exposed to malicious hackers and cyberterrorists who aim to cause significant damage to institutions and countries. Throughout the various stages of a cyber-attack, Open-source Intelligence (OSINT) tools could gather data from various publicly available platforms, and thus help hackers identify vulnerabilities and develop malware and attack strategies against targeted CI sectors. The purpose of the current study is to explore and identify the types of OSINT data that are useful for malicious individuals intending to conduct cyber-attacks against the CI industry. Applying and searching keyword queries in four open-source surface web platforms (Google, YouTube, Reddit, and Shodan), search results published between 2015 and 2020 were reviewed and qualitatively analyzed to categorize CI information that could be useful to hackers. Over 4000 results were analyzed from the open-source websites, 250 of which were found to provide information related to hacking and/or cybersecurity of CI facilities to malicious actors. Using thematic content analysis, we identified three major types of data malicious attackers could retrieve using OSINT tools: indirect reconnaissance data, proof-of-concept codes, and educational materials. The thematic results from this study reveal an increasing amount of open-source information useful for malicious attackers against industrial devices, as well as the need for programs, training, and policies required to protect and secure industrial systems and CI.