增强Microsoft 365安全性:集成数字取证分析以检测和减轻对抗性行为模式

IF 1.4 4区 医学 Q3 MEDICINE, LEGAL Forensic Sciences Research Pub Date : 2023-07-19 DOI:10.3390/forensicsci3030030
Marshall S. Rich
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引用次数: 0

摘要

本文研究了数字取证分析(DFA)技术在识别与Microsoft 365 (M365)环境中公共数据泄露或电子邮件地址泄露相关的恶意登录失败尝试的模式和趋势方面的有效性。模式识别技术用于分析安全日志,揭示对负面行为模式的见解。这些发现有助于数字取证、对立行为模式和基于云的网络安全方面的文献。实际影响包括制定有针对性的防御战略和确定普遍威胁的优先次序。未来的研究应将范围扩大到其他云服务和平台,通过更长的分析周期捕捉不断变化的趋势,并评估针对已确定的战术、技术和程序(TTPs)的具体缓解战略的有效性。
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Enhancing Microsoft 365 Security: Integrating Digital Forensics Analysis to Detect and Mitigate Adversarial Behavior Patterns
This research article investigates the effectiveness of digital forensics analysis (DFA) techniques in identifying patterns and trends in malicious failed login attempts linked to public data breaches or compromised email addresses in Microsoft 365 (M365) environments. Pattern recognition techniques are employed to analyze security logs, revealing insights into negative behavior patterns. The findings contribute to the literature on digital forensics, opposing behavior patterns, and cloud-based cybersecurity. Practical implications include the development of targeted defense strategies and the prioritization of prevalent threats. Future research should expand the scope to other cloud services and platforms, capture evolving trends through more prolonged and extended analysis periods, and assess the effectiveness of specific mitigation strategies for identified tactics, techniques, and procedures (TTPs).
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来源期刊
Forensic Sciences Research
Forensic Sciences Research MEDICINE, LEGAL-
CiteScore
3.60
自引率
7.70%
发文量
158
审稿时长
26 weeks
期刊最新文献
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