Decision Engine for Cyber Defense Configuration
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Solution Overview
Problem
Cyber defenders face challenges in selecting and configuring effective cyber defenses due to the complexity of integrating and managing Moving Target Defenses (MTDs), which are often manually handled without clear understanding of integration points and risks, leading to vulnerabilities and inefficiencies in defending against sophisticated cyber attacks.
Innovation Solution
A decision engine is developed, comprising a genetic algorithm framework, a parallelized reasoning framework, and a user interface, which automates the selection and configuration of cyber defenses by using a knowledge base of standard network configurations, attack surface reasoning algorithms, and human feedback to optimize security and cost tradeoffs, enabling efficient exploration and exploitation of defense configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection and configuration of cyber defenses is used, then human expertise and adaptability are leveraged, but the process is time-consuming and lacks systematic optimization
Solution Approach 1:
The system enables self-service through automated defense selection and configuration. The genetic algorithm framework automatically evaluates numerous defense configurations against attack surface models, selecting optimal configurations without requiring manual analysis of each option. This automation handles the time-consuming aspects while preserving expert guidance through the knowledge base.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. The genetic algorithm substitutes human iterative selection with automated evolutionary computation, while the attack surface reasoning algorithm replaces manual risk assessment with systematic computational analysis of security and cost tradeoffs.
2Reliability
If multiple Moving Target Defenses are integrated, then security coverage is improved, but the complexity of integration and management increases
Solution Approach 1:
The system segments the complex integration problem into manageable components: a knowledge base of standard network configurations, an attack surface model breaking down security into measurable dimensions, and a genetic algorithm that handles configuration combinations systematically. This segmentation makes the complex task of integrating multiple MTDs approachable and manageable.
Solution Approach 2:
The decision engine provides universal functionality by handling multiple defense integration scenarios through a single automated framework. The attack surface reasoning algorithm universally evaluates different defense configurations against standardized security and cost criteria, making the system adaptable to various integration needs without requiring separate processes for each scenario.
3Productivity
If automated decision-making is implemented, then efficiency and optimization are improved, but the need for human feedback and guidance increases
Solution Approach 1:
The system incorporates feedback mechanisms where users can provide guidance on the direction of evolution for the genetic algorithm search. Users can indicate whether to explore new configuration spaces or exploit promising areas, and can provide feedback on security and cost tradeoffs. This feedback loop maintains ease of operation by allowing users to guide the automated process according to their specific needs.
Data Source
AI summary
A decision engine includes: a genetic algorithm framework including a knowledge base of standard configurations, a candidate selector generator and a selector to select a candidate configuration from a plurality of preferred standard configurations in response to the candidate selector generator; a parallelized reasoning framework including an attack surface reasoning algorithm module to compute the security and cost tradeoffs of an attack surface associated with each candidate configuration; and a user interface framework including a web service engine where users can interact and provide feedback on direction of an evolution used in a genetic algorithm search for evolving defenses.