- The paper presents a novel method to generate synthetic geospatial trajectories using efficient abduction via a parsimony function and informed A* search.
- It employs annotated logic programs and graph models with a scalable heuristic derived from a parsimony bound to improve search efficiency.
- The approach generates trajectories robust against ML anomaly detectors, demonstrated through empirical tests and practical government evaluation.
Geospatial Trajectory Generation via Efficient Abduction
The paper "Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing" addresses the challenge of generating artificial human movement patterns that meet location and time constraints. This problem is pertinent to the security community, especially in contexts where preserving privacy while detecting such patterns is crucial. The research is framed as an instance of abduction guided by a novel parsimony function, represented as an aggregate truth value over an annotated logic program. The core of this research lies in efficiently abducing movement trajectories using an informed (A*) search algorithm, with implications for both practical deployment and theoretical advancements.
Contributions and Methodology
The authors' primary contributions can be summarized as follows:
- Modularity and Parsimony in Logic Programs: The paper introduces a parsimony function based on the aggregate truth values of logic programs and demonstrates that a subset of the program can effectively provide a lower bound on this parsimony requirement. This insight enables the implementation of an informed A* search, which enhances computational efficiency.
- Scalability: By leveraging the parsimony bound, the authors present a scalable heuristic function that streamlines the search. Empirical results validate the scalability of their approach, demonstrating its applicability to large datasets and complex scenarios.
- Robustness Against ML-based Detection: The approach generates movement trajectories indistinguishable by machine learning models trained to detect anomalies. Internal tests indicate that the trajectories often exhibit anomaly ratings comparable to or below the training data.
- Practical Deployment: The research outlines a cloud-based software stack, integrated with rule mining, graph databases, and AWS infrastructure, facilitating an efficient deployment for government-run evaluations. Notably, the system demonstrated a detection probability of less than 0.40 by machine learning models across various environments.
Technical Framework
The technical framework employs annotated logic with temporal extensions, integrating a novel parsimony function characterized by the aggregate truth values assigned to annotated logic programs. This framework is adapted to the geospatial domain by modeling geolocations as graphs, where nodes represent possible locations and edges depict traversable connections in a road network. The system utilizes a bottom-up learning approach to derive rules from historical data, which are subsequently employed in generating human movement trajectories.
A key theoretical result presented is the derivation of a lower bound on the parsimony function through a subset logic program, enabling an admissible heuristic for A* search. This result is pivotal in addressing the inherent complexity of logic-based abduction and enhancing scalability.
Empirical Evaluation
The paper provides extensive empirical evaluation, including internal assessments and independent government-run tests. The system's trajectory generation capability is evaluated using three types of curated geospatial data sets and simulated human activity across four geolocations. The robustness of the generated trajectories against an ensemble of machine learning anomaly detectors is also analyzed, illustrating the system's effectiveness in maintaining a low probability of detection.
Implications and Future Directions
The paper presents significant implications for the synthesis of human movement patterns, particularly in domains requiring privacy-sensitive data generation. The integration of logical reasoning with heuristic search represents a noteworthy advancement in modeling and simulation of geospatial phenomena.
Future research directions may include enhancing the interpolation procedure to minimize point detection anomalies and extending the logical framework to incorporate temporal factors such as time of day. Additionally, exploring neurosymbolic integration could bolster the system's capacity for direct ML anomaly detection integration.
In summary, this paper contributes a novel method for generating synthetic human movement patterns while addressing scalability and explainability challenges. Its deployment and evaluation in a government context underscore the approach's practical relevance and potential for broader application.