Real-Time Alert Generation via Dataset Manipulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems are inadequate in analyzing large datasets for complex events in network logs, leading to missed occurrences of potentially critical network security incidents, as they fail to efficiently identify and alert on coordinated access attempts across multiple user actions.
Innovation Solution
A data pipeline system that processes raw data sources into an ontology, allowing users to define alerts using object types and complex expressions, enabling robust analysis and real-time monitoring of alert definitions, with interactive user interfaces for experimentation and adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple expression searching is used to identify alerts in large datasets, then the system can operate with basic capabilities, but it fails to identify complex events and coordinated access attempts
Solution Approach 1:
The system segments complex alert definitions into reusable components including object types (user, device, network resource), actions (access, modify, delete), and conditions (time windows, frequency thresholds). This modular structure enables detection of complex coordinated access attempts while maintaining manageable system complexity through component reuse.
Solution Approach 2:
The system adds temporal and relational dimensions to alert detection by analyzing sequences of actions across multiple users and devices within time windows. This enables detection of coordinated attacks that span multiple dimensions (different users, different devices, different resources) rather than simple single-event detection.
2Measurement precision
If robust analysis with machine learning techniques is applied to identify complex events, then detection accuracy improves, but the time required for analysis increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining object types, actions, and conditions that can be reused across multiple alert definitions. This preparation work is done once and then rapidly applied to analyze large datasets, reducing the time required for complex event identification while maintaining high accuracy.
Solution Approach 2:
The system enables dynamic adjustment of analysis parameters such as time window size, frequency thresholds, and confidence levels. This allows optimization of the balance between detection accuracy and analysis time based on specific operational requirements, enabling rapid analysis when speed is critical while maintaining the option for deeper analysis when accuracy is paramount.
3Measurement precision
If users experiment with different alert definitions, then alert accuracy can be optimized, but the process becomes time-consuming without real-time feedback
Solution Approach 1:
The system provides real-time feedback during alert definition experimentation by showing users statistics on detected events, false positive rates, and alert distributions as they adjust parameters. This immediate feedback loop enables rapid optimization of alert definitions without time-consuming manual analysis, allowing users to iteratively refine their alert rules based on quantifiable performance metrics.
4Reliability
If comprehensive log analysis is performed on millions of user logs, then security monitoring coverage is improved, but the computational resources and time required become impractical
Solution Approach 1:
The system extracts and focuses analysis on specific object types, actions, and conditions relevant to security threats rather than analyzing all log data uniformly. By defining alerts around specific patterns (e.g., multiple failed login attempts, unusual access times, geographic anomalies), the system achieves comprehensive security monitoring coverage while processing only the relevant subset of data that matters for threat detection.
Data Source
AI summary
Systems and methods are provided for enhanced alert generation based on real-time manipulation of datasets. An example method includes accessing datasets including a plurality of rows each indicating a plurality of values. Occurrences of alert definitions are monitored via applying the alert definitions to the datasets, the alert definitions specifying expressions utilizing one or more of the values, with an occurrence of an alert definition indicating satisfaction of the specified expression and causing an associated alert to be generated for evaluation. Presentation of an interactive user interface associated with customizing alert definitions is caused. The interactive user interface presents a first visual portion, the first visual portion enabling specification of an expression for a customized alert definition. The interactive user interface presents a second visual portion, the second visual portion comparing occurrences of an existing alert definition with occurrences of a customized alert definition specified in the first visual portion.