Software Component Randomization for Reconnaissance Defense
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Solution Overview
Problem
Current computing systems are vulnerable to reconnaissance attacks as attackers can gather crucial information about operating systems and applications, which can be exploited to infiltrate the system, despite efforts to limit information disclosure.
Innovation Solution
The system generates unique configuration information to create randomized instances of software components, such as operating system kernels, applications, and libraries, with each instance having different operating characteristics, providing false information to attackers and evolving over time to prevent exploitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system provides consistent server information (name, version, configuration), then ease of operation and user trust are improved, but security against reconnaissance attacks deteriorates
Solution Approach 1:
The patent applies dynamics by making server information change over time through randomization. Instead of providing consistent, static server information, the system dynamically generates different configuration information for software components on different executions or time periods. This includes randomizing parameters such as port numbers, memory addresses, and configuration values, causing the server to appear different each time to prevent attackers from gathering reliable reconnaissance information.
Solution Approach 2:
The patent implements parameter changes by modifying configuration parameters of software components before execution. The system generates unique configuration information that varies across different instances or executions, changing parameters like port assignments, memory layouts, and operational characteristics. This ensures that even though the same software component is deployed, its observable parameters differ, thwarting reconnaissance efforts while maintaining functional consistency.
2Object-affected harmful factors
If the system randomizes software component configurations, then security against fingerprinting attacks is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by preparing and storing multiple pre-generated configuration variations before they are needed. The system pre-generates a pool of random configuration information and selects from these pre-prepared variations when deploying software components, rather than generating configurations on-demand during execution. This reduces runtime complexity while maintaining security benefits of randomization.
Solution Approach 2:
The patent uses copying by creating multiple copies of software components with different configuration information. Instead of modifying a single original component, the system generates copies that vary in their configuration parameters (such as different port numbers, memory addresses, or operational settings). These copies are then deployed to provide randomized behavior while the underlying component structure remains the same, simplifying management.
3Reliability
If the system generates multiple unique instances of software components, then reliability against exploitation is improved, but productivity and system performance deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the software component into modular units that can be independently configured and instantiated. Instead of creating entirely new software systems, the system segments the component into configurable parameters and core functionality, allowing efficient generation of multiple instances with different configurations. This modular approach maintains productivity while enabling reliable randomization for security.
Solution Approach 2:
The patent implements parameter changes by modifying non-critical configuration parameters rather than fundamentally changing software behavior. The randomization affects observable parameters like port numbers, memory addresses, and configuration values, while the core execution logic and performance characteristics remain optimized and consistent. This ensures multiple unique instances can be generated without sacrificing system productivity or execution efficiency.
Data Source
AI summary
An example method includes generating, by a computing system, first unique configuration information, generating, by the computing system and based on the first unique configuration information, a first unique instance of a software component, generating second unique configuration information, wherein the second unique configuration information is different from the first unique configuration information, and generating, based on the second unique configuration information, a second unique instance of the software component that is executable on the runtime computing system. The first and second unique instances of the software component comprise different instances of the same software component that each are configured to have uniquely different operating characteristics during execution on the runtime computing system.