Attack Stage Prediction Models From Threat Intelligence Reports
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Solution Overview
Problem
Existing security solutions fail to predict future attack steps in cyberattacks, leading to prolonged detection and response times, making organizations vulnerable to threats like data theft and ransomware.
Innovation Solution
A system that utilizes linguistic analysis on threat intelligence reports to determine cause-and-effect relationships between attack stages, generating probability models to predict future attack phases and provide actionable insights using AI-powered decision-making tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional security solutions are used to detect and respond to attacks based on current and historical evidence, then the detection and response process can be performed, but the time required is too long and the solutions cannot predict attacker's next steps
Solution Approach 1:
The system performs preliminary action by predicting future attack stages before they occur. The probability model and transition matrix analyze current attack stages and threat intelligence data to forecast what attack stages will occur next, allowing security teams to prepare preventive measures in advance rather than merely reacting to historical patterns
Solution Approach 2:
The system implements feedback by continuously updating the probability model with new threat intelligence reports and attack stage data. The transition matrix is refined based on observed attack patterns, creating a learning system that improves its prediction accuracy over time by incorporating feedback from actual attack outcomes and emerging threat patterns
2Reliability
If existing security solutions rely on current and historical evidence without prediction capability, then the system architecture remains simple, but the ability to provide proactive security measures is lost
Solution Approach 1:
The system introduces intermediary components including a transition matrix that models attack stage relationships and a probability model that bridges current attack evidence with future predictions. These intermediaries translate raw threat intelligence and attack stage data into actionable prediction insights without requiring complete system redesign
Solution Approach 2:
The system applies parameter changes by transforming static security detection into dynamic prediction through probabilistic modeling. The transition matrix uses probability parameters to represent the likelihood of transitioning between attack stages, allowing the system to adapt its security posture based on changing threat landscapes and attack patterns
3Object-affected harmful factors
If organizations wait for attacks to be detected and investigated using conventional methods, then resource allocation remains straightforward, but vulnerability to data theft and ransomware increases
Solution Approach 1:
The system performs preliminary anti-action by predicting future attack stages and enabling security teams to take preventive measures before attacks succeed. By forecasting what attack stages will occur next based on current evidence and threat intelligence, the system allows organizations to implement countermeasures in advance, neutralizing threats before they can cause data theft or ransomware infections
Data Source
AI summary
A method for predicting a future stage of an attack on a computer system. The method comprises performing, by the computer system, linguistic analysis on threat intelligence reports, where the threat intelligence reports comprise known stages of the attack. The method also comprises processing, by the computer system, the linguistic analysis with a transition matrix to determine probabilities of cause-and-effect relationships between the known stages of the attack, updating, by the computer system, a probability model based on the probabilities determined by the transition matrix, and predicting, by the computer system, the future stage of the attack based on the probability model and attack classifications.