Management Controller Anomaly Detection Using Deep Learning
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Solution Overview
Problem
Information Technology companies face challenges in providing computing devices with high availability and security, as systems can become infected with malware that penetrates physical boundaries and proliferates within networks, posing internal threats that modern security practices struggle to detect and mitigate effectively.
Innovation Solution
A management controller leveraging out-of-band positioning and deep learning models, such as Recurrent Neural Networks, monitors health information from computing devices to detect anomalies, alert administrators, and take proactive measures like isolating network traffic or updating models based on feedback to enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used to detect anomalies in real-time, then detection accuracy and security are improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by training deep learning models offline using historical data and known anomaly patterns. The trained models are then deployed for real-time inference, separating the computationally intensive training phase from the execution phase. This allows complex anomaly detection to be performed without real-time computational overhead during actual system operation.
Solution Approach 2:
The system implements feedback mechanisms where detected anomalies are fed back into the training process to continuously improve model accuracy. Performance metrics from real-time detection are used to retrain and refine the deep learning models, creating a self-improving system that maintains high detection accuracy while adapting to evolving threat patterns.
2Reliability
If real-time monitoring of health information is implemented, then system security and availability are improved, but energy consumption and processing resources increase
Solution Approach 1:
The system employs periodic monitoring instead of continuous monitoring, sampling health information at strategically determined intervals. The deep learning models process this periodic data to detect anomalies, reducing computational load and energy consumption while maintaining effective security monitoring. The timing and frequency of sampling are optimized to balance detection effectiveness with resource consumption.
3Difficulty of detecting and measuring
If multiple monitoring parameters are collected and analyzed, then detection capability is improved, but data processing complexity and storage requirements increase
Solution Approach 1:
The system extracts and selects only the most relevant health information parameters from the vast amount of available data. The deep learning models are trained to identify and focus on critical parameters that indicate anomalies, filtering out redundant or less informative data. This extraction process reduces the quantity of data that needs to be processed and stored while maintaining high detection capability for meaningful anomalies.
Data Source
AI summary
Examples disclosed herein relate to processing health information of a computing device according to a deep learning model to determine whether an anomaly has occurred. Multiple computing devices can be part of a system. One of the computing devices includes a host processing element, a management controller separate from the host processing element, and a deep learning model that includes parameters that are trained to identify anomalistic behavior for the computing device. The management controller can receive health information from multiple components of the computing device and process the health information according to the deep learning model to determine whether an anomaly occurred.