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About Mining Temporal Attack Patterns From Cyberthreat Intelligence Reports
This document summarizes a research paper that proposes ChronoCTI, a machine learning pipeline for mining temporal attack patterns from cyberthreat intelligence reports. The pipeline uses natural language processing and machine learning to identify actions and extract temporal relations between actions described in unstructured text reports of past cyberattacks. The researchers applied ChronoCTI to 713 cyberthreat reports to identify 124 temporal attack patterns across nine categories. Most common patterns involved tricking users into executing malicious code or bypassing anti-malware systems. The identified patterns could help practitioners prioritize defenses and think like attackers to strengthen cybersecurity.
Detailed Information
| Author: | ['patricio silva'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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