Machine Learning Model Detecting Missing Security Alerts
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Solution Overview
Problem
Current threat detection techniques in cloud computing systems may miss malicious activity, particularly if a security alert corresponding to a step in an attack campaign is not detected, leading to incomplete incident reporting and potential undetected vulnerabilities.
Innovation Solution
The system employs a machine learning model to analyze alert sequences and identify missing security alerts by comparing them to a predetermined sequence of alerts associated with known security incidents, thereby generating notifications for missing alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional alert sequence matching is used to identify security incidents, then the system operates with simple rule-based logic, but it fails to detect incidents with missing alerts in the sequence
Solution Approach 1:
A machine learning model is introduced as an intermediary between the alert sequence input and the security incident detection output. The model learns complex patterns from historical data and can identify incidents even when alerts are missing from the sequence, thereby improving detection reliability without requiring complex manual rule engineering
Solution Approach 2:
The system performs preliminary training of the machine learning model using historical alert sequences and known security incidents before deployment. This preliminary action allows the model to learn detection patterns in advance, enabling it to reliably identify incidents with missing alerts when deployed in production
2Loss of time
If the system waits for complete alert sequences before detecting incidents, then false positives are reduced, but detection time increases and vulnerabilities remain undetected longer
Solution Approach 1:
The machine learning model is trained to detect security incidents based on partial alert sequences rather than requiring complete sequences. The model can identify incidents even when some alerts are missing, allowing detection to occur earlier in the attack lifecycle while maintaining acceptable accuracy through learned patterns from historical data
3Productivity
If manual analysis of alert sequences is performed to identify missing alerts, then precision can be maintained, but productivity decreases due to high resource consumption
Solution Approach 1:
The system replaces manual mechanical analysis of alert sequences with an automated machine learning model. The model automatically compares incoming alert sequences against learned patterns from historical incidents, identifying missing alerts efficiently without human intervention, thereby improving productivity while maintaining information completeness through systematic pattern recognition
Data Source
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AI summary
Methods, systems, and apparatuses are provided for detecting a missing security alert by receiving an alert sequence generated by a network security provider, applying the received alert sequence to a security incident model, receiving an indication from the security incident model that the received alert sequence corresponds to a security incident defined by a predetermined sequence of alerts that includes at least one alert missing from the received alert sequence, and generating a notification to the network security provider that indicates at least one of the security incident or the missing alert(s). In addition, the security incident model may be generated by providing a set of historical alerts and a set of historical security incidents to a machine learning algorithm to generate the security incident model.