Compute Resource Auto-Healing Through Static-to-Runtime Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud computing platforms are vulnerable to malicious code that compromises compute resources, leading to performance degradation and security breaches, with conventional detection and mitigation techniques requiring lengthy diagnostics and significant downtime.
Innovation Solution
A system and method for detecting compromised compute resources by statically analyzing image files and comparing them with runtime characteristics, enabling automatic remedial actions such as restarting or reinstalling the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection and mitigation techniques are used, then detection accuracy is improved, but downtime and diagnostic time increase significantly
Solution Approach 1:
The system performs preliminary actions by creating baseline profiles of normal application behavior during deployment and by pre-configuring auto-healing scripts and remediation playbooks. When anomalies are detected, the system can immediately execute pre-prepared remediation actions without requiring lengthy diagnostic procedures, thus reducing downtime while maintaining accurate detection capabilities
Solution Approach 2:
The system implements automated anomaly detection and response that skips traditional lengthy diagnostic steps by using machine learning models to rapidly analyze behavior patterns and directly trigger remediation actions. The auto-healing mechanism rushes through the mitigation process by automatically isolating compromised resources and restoring services without manual intervention, significantly reducing the time lost to diagnostics
2Productivity
If cloud computing platforms provide massive computing resources, then service performance is improved, but vulnerability to malicious code exploitation increases
Solution Approach 1:
The system implements continuous feedback loops where machine learning models constantly monitor application behavior, compare it against baseline profiles, and automatically adjust security policies. When malicious code is detected, the system provides immediate feedback by triggering isolation mechanisms and updating detection rules in real-time, enabling the platform to maintain high resource utilization while dynamically responding to threats
Solution Approach 2:
The system introduces an intermediary layer consisting of behavior analysis agents and auto-healing orchestration services that mediate between computing resources and potential threats. These intermediaries monitor resource usage patterns, detect anomalies indicative of malicious code, and coordinate automated responses that protect the platform while allowing legitimate high-performance operations to continue uninterrupted
3Speed
If static analysis of image files is performed, then detection speed is improved, but detection completeness may be reduced compared to dynamic analysis
Solution Approach 1:
The system merges static analysis of image files with dynamic behavior monitoring by combining baseline profiles created from static analysis with real-time runtime behavior data. This hybrid approach leverages the speed of static analysis for initial detection while using dynamic behavior analysis to confirm threats and maintain high detection completeness, achieving both rapid response and accurate identification of malicious code
Data Source
AI summary
Embodiments described herein are directed to determining whether an application executing on a compute instance has been corrupted or compromised by malicious code. This may achieved by statically analyzing an image file from which the application is based to determine characteristics thereof. Such characteristics are representative of the behavior that is expected to be performed by the application during execution. During execution of the application, runtime characteristics of the application are determined, which are determined based on an analysis of the address space in memory allocated for a computing process of the application. The statically-determined characteristics are compared to the determined runtime characteristics to determine discrepancies therebetween. In the event that a discrepancy is found, a determination is made that the application has been compromised or corrupted and an appropriate remedial action is automatically performed.