Memory Allocation Baseline for Code Injection Detection
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Solution Overview
Problem
Existing systems face challenges in detecting and mitigating potential security risks, particularly code injection attacks, which can compromise computing systems by altering memory allocation patterns, making it difficult to distinguish between normal and anomalous behavior.
Innovation Solution
Implementing a system that collects and analyzes memory allocation data using machine learning techniques, both supervised and unsupervised, to establish baselines and identify anomalies, flagging processes with unusual memory allocation for further investigation and potential interdiction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large number of executable code modules and libraries are deployed within a computing environment, then quality of service is maintained under various operating conditions, but security and integrity of deployed files become difficult to ensure
Solution Approach 1:
The system performs preliminary actions by establishing a baseline of normal memory allocation patterns for each process before security incidents occur. It continuously monitors memory allocation and compares current patterns against the established baseline to detect anomalies, enabling proactive security monitoring without disrupting normal service operations.
Solution Approach 2:
The system introduces an intermediary monitoring layer that sits between the deployed code modules and the operating system memory management. This intermediary collects memory allocation data, analyzes it against baselines, and flags potential security issues without interfering with the actual execution of legitimate code, thus maintaining quality of service while enhancing security monitoring.
2Reliability
If memory allocation data is continuously collected and analyzed to detect code injection attacks, then security detection capability is improved, but system performance and resource consumption increase
Solution Approach 1:
The system applies local quality by tailoring memory allocation baseline analysis to each individual process based on its specific characteristics and normal behavior patterns. Rather than using a single universal baseline, it creates process-specific baselines that account for variations in memory usage across different processes, reducing false positives and improving detection accuracy without requiring excessive computational resources.
Solution Approach 2:
The system utilizes parameter changes by monitoring variations in memory allocation parameters over time and comparing them against established baselines. It detects security incidents by identifying statistically significant deviations from normal patterns, allowing for efficient anomaly detection without requiring continuous heavy computational analysis of all memory operations.
Data Source
AI summary
Techniques are described for identifying anomalous execution instance of the process as a security risk by analyzing the memory allocation for the process. Performance data describing memory allocation data describing the amount of memory allocated for a process or utilized by a process during its execution. A baseline of memory allocation is established for the plurality of executions of the process by applying a statistical distribution to the performance data collected. A memory allocation for the executing process may be compared to the baseline. An anomalous execution instance of the process that is outside a predetermined number of statistical variances of the baseline may be determined. At least one anomalous execution instance of the process may be designated as a security risk based at least partly on the anomalous memory allocation.