The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Exploiting Trust: Stealthy Attacks Through Socioware and Insider Threats
Abstract
Online social networks (OSNs) provide a new dimension to people's lives by giving birth to online societies. OSNs have revolutionized the human experience, but they have also created a platform for attackers to distribute infections and conduct cybercrime. An OSN provides an opportunistic attack platform for cybercriminals through which they can spread infections at a large scale. We describe a category of malware (or attacks) known as socioware that exploits OSN environments for performing unauthorized and nefarious activities. Socioware can be an executable, an extension, an exploit code, etc., that conducts malicious operations in OSNs with serious impact on users. Furthermore, we discuss the socioware taxonomy highlighting the characteristics of socioware to illustrate the design and exploitation tactics of OSN malware. In contrast, insider threats (employees or contractors) are posing a grave threat to organizations, with a motivation to steal critical data and monetize it for financial gains. Insider threats have become a serious concern for many organizations today. We present a complete attack model to demonstrate how an insider threat exploits the online trust and confidentiality by transforming an OSN into a socioware distribution platform that infects other employees' systems. Finally, we discuss security defenses that can be adopted to defend against socioware.
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In Prime MotorX's perception layer, patch management failure and weak authentication in thermocouple firmware are exploited by ransomware and malware attacks.
“In this scenario, the exploited vulnerabilities are Patch Management Failure and Weak Authentication.The launched attacks include Ransomware [45], [46], and Malware [47], with the target being the Firmware.”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [46]
Citations
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Eyeleko, A. H., & Feng, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308195 · Cited by 38
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Metadata
- Authors
- Sood, A. K., Zeadally, S., & Bansal, R.
- Affiliations
- Capgemini (United States) (US)
- University of Kentucky (US)
- Venue
- IEEE Systems Journal
- Published
- 2017-06
- Volume
- 11
- Issue
- 2
- Pages
- 415-426
- ISSN
- 1932-8184 (ISSN-L)
- 1937-9234
- 2373-7816
- OpenAlex
- W2312254734
- Cited by
- 11
- References
- 22
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 2 · 2021: 1 · 2020: 1 · 2018: 1
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Systems Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Law › Cybercrime and Law Enforcement Studies
- Keywords
- insider threats, social networking sites, malware, cybercrime, attack models, data theft, security defenses
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