Adaptive Network Decoys for Evolving Attacker Preferences
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Solution Overview
Problem
Existing network decoys become stale and ineffective as attackers become sophisticated, and simply alternating between predetermined decoys does not effectively deter advanced attackers.
Innovation Solution
A method of maintaining a pool of dynamic and adaptive network decoys by monitoring interactions with attackers to learn their preferences, dynamically creating new decoys that are increasingly attractive, and evolving the decoy pool using machine learning techniques and evolutionary processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predetermined decoys are deployed on the network, then network security is enhanced by providing targets for attackers, but the decoys become stale and ineffective as attackers become sophisticated
Solution Approach 1:
The patent implements dynamic decoy deployment by continuously monitoring attacker interactions and automatically updating decoy configurations in real-time. The system transitions from static predetermined decoys to dynamic adaptive decoys that evolve their characteristics based on observed attacker behavior, ensuring ongoing effectiveness against sophisticated threats
Solution Approach 2:
The system changes key parameters of decoys including their configuration, services offered, and behavioral characteristics based on monitored attacker preferences. By dynamically adjusting these parameters according to learned attacker patterns, the decoys maintain their attractiveness and effectiveness while adapting to evolving attack methodologies
2Reliability
If the decoy pool is updated dynamically based on attacker interactions, then the decoys remain effective against sophisticated attackers, but the system complexity increases
Solution Approach 1:
The patent implements a feedback loop where attacker interactions with decoys are continuously monitored and analyzed. This feedback informs automatic updates to the decoy pool, creating a closed-loop system that adapts to attacker behavior. The feedback mechanism enables the system to maintain effectiveness without requiring complex manual intervention by security personnel
Solution Approach 2:
The system performs self-updating of the decoy pool by automatically monitoring attacker behavior, analyzing preferences, and generating updated decoy configurations without external intervention. This self-service capability reduces operational complexity while maintaining high adaptability to evolving threats
3Adaptability or versatility
If machine learning techniques are used to evolve decoys, then the system can learn and adapt to attacker preferences, but the computational resources and time required increase
Solution Approach 1:
The system pre-generates multiple candidate decoy configurations and pre-computes potential evolutionary paths using machine learning models. By preparing these options in advance, the system can rapidly select and deploy appropriate decoys when attacker interactions are detected, reducing the time penalty associated with real-time adaptation
Data Source
AI summary
A method of maintaining a pool of network decoys includes evolving, over a plurality of epochs, the pool of network decoys towards one or more preferences of a network attacker. The method includes modeling preferences of the network attacker based on monitored interactions between the network attacker and the pool of network decoys to generate a preference model of the network attacker. Each epoch includes updating a fitness function based on the preference model and applying the fitness function to each network decoy included in the pool of network decoys to determine a plurality of fitness values, where the fitness values are representative of an attractiveness of a respective network decoy to the network attacker. The pool of network decoys is then updated based on the fitness values.