Alert Pattern Prediction for Early Security Incident Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional security systems struggle to effectively identify potential security issues from patterns of alerts, often leading to delayed responses and potential data breaches, as individual alerts may not clearly indicate malicious activity, and security analysts are overwhelmed by numerous alerts.
Innovation Solution
A predictive model is used to analyze patterns of triggered alerts to forecast a next alert, providing security analysts with information on the likelihood and timing of potential security incidents, allowing proactive mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional security systems monitor all alerts individually, then security analysts can detect potential security issues, but security analysts are overwhelmed by the large number of alerts and cannot effectively identify real security issues
Solution Approach 1:
The patent segments the large volume of alerts into smaller meaningful units by identifying patterns and groupings. Instead of analyzing each alert individually, the system clusters related alerts together based on temporal proximity, source, and type, creating manageable segments that analysts can review efficiently while maintaining detection accuracy.
Solution Approach 2:
The patent introduces an intermediary processing layer between alert generation and analyst review. This intermediary system automatically analyzes alert patterns, predicts future alerts, and prioritizes sequences, acting as a mediator that filters and prepares alert information before presenting it to security analysts, thereby reducing their workload while improving detection precision.
2Loss of time
If security analysts review alerts after they are triggered, then they can respond to security issues, but by the time alerts are triggered, damage may have already been inflicted
Solution Approach 1:
The patent implements preliminary action by predicting future alerts based on patterns in current alert sequences. The system analyzes temporal patterns and alert relationships to forecast what alerts are likely to occur next, allowing security teams to take preventive actions before the actual security incident manifests, thereby reducing both response time and potential damage severity.
Solution Approach 2:
The patent applies preliminary anti-action by identifying malicious patterns early in the alert sequence and taking countermeasures before the complete attack chain executes. By detecting anomalous patterns and predicting their progression, the system enables preemptive security responses that neutralize threats before they can cause maximum harm.
3Reliability
If the system predicts next alerts speculatively, then security issues can be identified before they occur, but false predictions may increase analyst workload
Solution Approach 1:
The patent applies partial action by implementing predictive functionality selectively rather than universally. The system predicts next alerts only for specific patterns and contexts where historical data suggests high confidence in predictions, rather than attempting to predict all possible alert sequences. This approach maintains detection reliability while avoiding the complexity and false positives that would result from exhaustive prediction attempts.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed herein is a system for predicting, given a pattern of triggered alerts, a next alert in order to identify malicious activity that is about to occur on resource(s) being monitored by a security operations center. A resource can include a server, a storage device, a user device (e.g., a personal computer, a tablet computer, a smartphone, etc.), a virtual machine, networking equipment, etc. Accordingly, the next alert is speculatively triggered in advance and a security analyst can be notified of a pattern of activity that is likely to be malicious. The security analyst can then investigate the pattern of triggered alerts and the speculatively triggered alert to determine whether steps to mitigate the malicious activity before it occurs should be taken.