Virtual Machine Code Introspection for Cloud Security
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Solution Overview
Problem
Cloud computing environments face challenges in securing data against malicious or defective external applications, as conventional techniques fail to detect and mitigate malicious code in real-time, leading to potential data exposure and exploitation.
Innovation Solution
A system that performs dynamic process code introspection on a virtual machine to detect and mitigate information disclosure and security events through real-time function execution tracing and a scoring system, which updates a function call pattern blacklist using machine learning, heuristics, and probabilistic models to prevent unauthorized data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security techniques are used in cloud computing environments, then device complexity is reduced, but security reliability deteriorates as malicious or defective external applications can expose private data without detection
Solution Approach 1:
The patent implements nested virtualization by placing a virtual machine (VM) inside another VM, creating a hierarchical security architecture. The outer VM acts as a secure container that hosts the inner VM, which executes external applications. This nesting structure enables the outer VM to monitor and control the inner VM's operations, providing deep inspection capabilities while maintaining system security without requiring complete redesign of the cloud infrastructure
Solution Approach 2:
The patent introduces an intermediary scoring engine that acts as a mediator between the application code and the execution environment. This scoring engine receives function call information from the VM, evaluates it against learned patterns and security policies, and determines whether to allow or block execution. The intermediary layer provides intelligent security filtering without requiring direct modification of the application code or complete system overhaul
2Measurement precision
If real-time code inspection is implemented, then security detection capability is improved, but processing time increases due to dynamic analysis requirements
Solution Approach 1:
The patent implements preliminary action by pre-compiling application code into bytecode and pre-loading it into the virtual machine before execution. The scoring engine also pre-loads trained machine learning models and security policies into memory. This preliminary preparation ensures that when code execution begins, the inspection mechanisms are already in place and ready to operate, minimizing startup delays and enabling real-time inspection without significant processing overhead
Solution Approach 2:
The patent replaces traditional mechanical static code analysis with dynamic machine learning-based scoring. Instead of using rule-based static inspection that requires thorough code examination, the system uses trained machine learning models that can rapidly evaluate code behavior patterns in real-time. This substitution enables faster detection by leveraging the scoring engine's ability to process and evaluate code dynamically based on learned patterns rather than exhaustive static analysis
3Reliability
If dynamic code execution monitoring is performed, then malicious code detection is improved, but system resource consumption increases due to continuous tracing and scoring operations
Solution Approach 1:
The patent extracts the intensive security analysis functions from the main execution path and places them in a separate scoring engine. The VM instrumented code generates function call information that is sent to the scoring engine for evaluation. This extraction allows the main execution environment to operate with minimal overhead while the scoring engine handles the computationally intensive tasks of pattern matching, machine learning inference, and security policy evaluation in parallel, reducing the resource burden on the primary system
Solution Approach 2:
The patent implements self-service by having the scoring engine automatically learn from executed code patterns and update its security models without external intervention. The machine learning models are continuously trained on observed code behavior, allowing the system to improve its detection capabilities autonomously. This self-learning mechanism reduces the need for manual security updates and external configuration, enabling the system to maintain high detection reliability while optimizing resource usage based on learned patterns
Data Source
AI summary
Computer programming code may be executed via look ahead execution in a virtual machine. The computer programming code may include a first instruction to retrieve data stored in an on-demand computing services environment and a second instruction to transmit the data to a recipient. The first instruction, the second instruction, and the data may be evaluated to determine whether the execution of the computer programming code constitutes acceptable use of the on-demand computing services environment. When it is determined that the execution of the computer programming code does not constitute acceptable use of the on-demand computing services environment, further execution of the computer programming code may be halted.