Exploring Vulnerabilities: An In-Depth Analysis of Security Weaknesses Leading to Dark Web Exposure and Improved Security Defenses Using Artificial Intelligence (AI) & Machine Learning (ML)
Keywords:
Personal Identifiable Information, Artificial Intelligence, Cybersecurity, Dark Web, CybercrimeSynopsis
This is a Chapter in:
Book:
Competitive Tools, Techniques, and Methods
Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9
Series:
Chronicle of Computing
Chapter Abstract:
In modern society, demand and value of stolen personally identifiable information (PII) has risen dramatically. A significant portion of this information can be found within the Dark Web. Growing exposure and exchange of PII is largely a resultant of inadequate organizational security measures. This investigation uses Artificial Intelligence (AI) and Machine Learning (ML) to explore the underlying causes of data breach events to examine how adaptation of more robust security strategies can mitigate PII exposure.
Cite this paper as:
Squillace J., Cappella J., Sepp A. (2024) Exploring Vulnerabilities: An In-Depth Analysis of Security Weaknesses Leading to Dark Web Exposure and Improved Security Defenses Using Artificial Intelligence (AI) & Machine Learning (ML).
In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIF24#15. https://doi.org/10.55432/978-1-6692-0007-9_5
Presented at:
The 2024 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024
Contact:
Joseph Squillace
jms10943@psu.edu
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