探讨网络安全数据泄露中的关键问题:用基于ml的文本分析分析数据泄露诉讼

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Dominik Molitor, Wullianallur Raghupathi, Aditya Saharia, Viju Raghupathi
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引用次数: 0

摘要

虽然数据泄露是一种频繁而普遍的现象,但数据泄露的特征和维度尚未得到探索。在这项新颖的探索性研究中,我们将机器学习(ML)和文本分析应用于数据泄露诉讼案件的综合收集,以从这些案件中包含的叙述中提取见解。我们的分析显示,利益相关者(例如诉讼当事人)关注的主要话题涉及身份盗窃、黑客、疏忽、FCRA(公平信用报告法案)、网络安全、保险、电话设备、TCPA(电话消费者保护法)、信用卡、商家、隐私等。这些话题可分为四大类:“电话诈骗”、“网络安全”、“身份盗窃”和“商业数据泄露”。通过使用机器学习、文本分析和描述性数据可视化,我们的研究为全面分析大型文本数据集提供了基础。这些发现对网络安全领域的研究人员和从业人员,尤其是那些正在应对数据泄露挑战的人来说,都具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Key Issues in Cybersecurity Data Breaches: Analyzing Data Breach Litigation with ML-Based Text Analytics
While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: “phone scams”, “cybersecurity”, “identity theft”, and “business data breach”. By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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