File Event Correlation for Intrusion Alert Remediation
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Solution Overview
Problem
Current intrusion detection systems (IDS) lack the ability to correlate malware signatures with actual hosts, requiring resource-intensive and time-consuming processes, and do not provide fine-grained control over file events in virtual computing instances, making it inefficient for users to manually remediate malware attacks.
Innovation Solution
A system and method that correlates file events with intrusion detection alerts to generate event-correlation data, using a machine learning component to analyze real-time data and provide users with recommended response actions, thereby automating the remediation process and reducing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of intrusion alerts is performed, then accuracy in determining appropriate response actions can be achieved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically analyzing file events, correlating them with intrusion alerts, and generating remediation recommendations before human users need to manually investigate. This pre-computation of analysis results significantly reduces the time users need to spend on alert remediation while maintaining accuracy through systematic correlation of security events with file system activities.
2Measurement precision
If comprehensive file event monitoring is implemented, then ability to correlate malware signatures with actual hosts improves, but network bandwidth usage and system resources increase
Solution Approach 1:
The system extracts only the essential and relevant file events needed for malware correlation rather than monitoring and transmitting all possible system events. By selectively extracting critical file operations related to malware behavior, the system achieves accurate host-malware correlation while minimizing network bandwidth consumption and system resource overhead.
3Productivity
If automated response actions are implemented, then productivity in handling intrusion alerts improves, but risk of incorrect actions increases
Solution Approach 1:
The system incorporates feedback mechanisms where automated analysis of file events continuously refines the correlation between malware signatures and host activities. This feedback loop enables the system to learn from observed patterns and improve the accuracy of its automated remediation recommendations over time, increasing reliability while maintaining high productivity in alert handling.
Data Source
AI summary
File events are correlated with intrusion detection alerts for corrective action. A monitoring component receives file events from a thin agent. An analysis component analyzes the file events and metadata obtained from the intrusion detection alerts, such as attack type or file name, to correlate a set of file events to at least one detected action (intrusion) described in the alert. A recommendation component identifies one or more options, including one or more corrective actions, which are applicable for remediating the alert. The set of options includes a recommended action from two or more possible corrective actions. The set of options are output or displayed to the user. The user selects which option/action to perform in response to the alert. In some examples, an automatic response is performed without user selection with respect to selected types of alerts, detected action(s), selected file(s) or other user-generated criteria.