Deception Profiler Optimizes Network Security Deployment
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Solution Overview
Problem
Conventional network deception mechanisms are costly and inefficient due to the difficulty in determining the optimal location, number, configuration, and deployment schedule for deception mechanisms, leading to either ineffective attacker diversion or excessive resource expenditure.
Innovation Solution
A method for intelligently deploying deception mechanisms using a deception profiler that assesses asset densities, historical attack statistics, and machine information to determine the appropriate number, configuration, and schedule for deception mechanisms within a network, allowing for targeted and dynamic deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deception mechanisms are deployed to divert attackers from real assets, then network security effectiveness is improved, but deployment and maintenance costs increase
Solution Approach 1:
The system dynamically adjusts the number and placement of deception mechanisms based on real-time asset density calculations and historical attack patterns. The deception profiler continuously learns from new attacks and modifies deployment strategies, transforming static deception mechanisms into adaptive security assets that optimize their presence based on network conditions and threat levels.
Solution Approach 2:
The system changes key parameters including asset density thresholds, deception mechanism counts, and deployment locations based on computed importance scores. By adjusting these parameters dynamically rather than using fixed configurations, the system achieves better security coverage while reducing unnecessary deployment costs in low-risk areas.
2Reliability
If more deception mechanisms are deployed to improve attacker diversion, then security coverage is improved, but resource expenditure increases
Solution Approach 1:
The system applies different densities of deception mechanisms to different network segments based on local asset density and importance scores. High-value areas with critical assets receive higher concentrations of deception mechanisms, while low-value areas receive fewer or no mechanisms, optimizing the overall distribution rather than using a uniform approach across the entire network.
Solution Approach 2:
The system determines the optimal number of deception mechanisms by analyzing historical attack data and asset criticality, deploying enough mechanisms to effectively divert attackers from critical assets without over-deploying in areas where additional mechanisms would provide diminishing returns. This partial action approach avoids the waste of excessive deployment while maintaining adequate coverage.
3Reliability
If deception mechanisms are deployed in all network locations to maximize protection, then security coverage is improved, but system complexity increases
Solution Approach 1:
The system segments the network into different zones based on asset density and criticality, applying different deception mechanism deployment strategies to each segment. This segmentation approach simplifies the overall deployment by breaking down the complex problem of network-wide protection into manageable regional decisions, each optimized based on local characteristics.
Solution Approach 2:
The deception profiler performs preliminary analysis of network assets, historical attacks, and threat patterns before deployment decisions are made. By pre-computing asset densities, importance scores, and optimal deployment configurations, the system simplifies the actual deployment process and reduces on-the-fly complexity during implementation.
4Reliability
If deception mechanisms are continuously monitored and adjusted to maintain effectiveness, then security effectiveness is improved, but monitoring and maintenance costs increase
Solution Approach 1:
The system implements continuous feedback loops where the deception profiler monitors new attacks, learns from attack patterns, and automatically adjusts deception mechanism deployment accordingly. This automated feedback mechanism maintains effectiveness without requiring proportional increases in human monitoring and maintenance resources, as the system self-optimizes based on incoming data.
Solution Approach 2:
The deception profiler performs self-learning and self-adjustment by analyzing attack data and automatically modifying deployment strategies. This self-service capability reduces the need for external monitoring and manual maintenance, lowering operational costs while maintaining or improving effectiveness over time.
Data Source
AI summary
Methods, systems, and computer-readable mediums are described herein to provide context-aware knowledge systems and methods for deploying deception mechanisms. In some examples, a deception profiler can be used to intelligently deploy the deception mechanisms for a network. For example, a method can include identifying a network for which to deploy one or more deception mechanisms. In such an example, a deception mechanism can emulate one or more characteristics of a machine on the network. The method can further include determining one or more asset densities and a summary statistic. An asset density can be associated with a number of assets connected to the network. The summary statistic can be associated with a number of historical attacks on the network. Using at least one or more of the one or more asset densities, the summary statistic, other information associated with the network, or a combination thereof, the method can further include determining a number of deception mechanisms to deploy, and deploying the number of deception mechanisms.