Specializing Context-Free Grammars With a (1 + 1)-EA

Manzoni, Luca; Bartoli, Alberto; Castelli, Mauro; Gonçalves, Ivo; Medvet, Eric

doi:10.1109/tevc.2020.2983664

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Moreover, the range of temporal operators is limited to [0, 9]. Other means could be used to impact on the complexity of evolved formulas as, e.g., using a different grammar for the same language with repeated production rules [22]-as shown in [18], this could result in better evolvability.…”

Section: Methodsmentioning

confidence: 99%

“…In line 12, a single child individual is generated by the selectAndReproduceWithEnfDiv procedure, using tournament selection with enforced-diversity. In the solutions update step (lines [15][16][17][18][19][20][21][22][23][24][25], the variables of the individuals that satisfies the isSolution condition are considered and added to the set of solved variables V solved ; all individuals that contain at least one of these variables are extracted from the population and added to the solutions ensemble S. If necessary, the population is refilled with newly generated individuals (lines 26-28). The stop condition (line 5) counts the number of distinct variables in V solved and, stops the iterative algorithm if V solved ≥ n target .…”

Section: Methodsmentioning

confidence: 99%

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One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems

Indri

Bartoli

Medvet

et al. 2022

Lecture Notes in Computer Science

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Cyber-Physical Systems (CPS) are prevalent in critical infrastructures and a prime target for cyber-attacks. Multivariate time series data generated by sensors and actuators of a CPS can be monitored for detecting cyber-attacks that introduce anomalies in those data. We use Signal Temporal Logic (STL) formulas to tightly describe the normal behavior of a CPS, identifying data instances that do not satisfy the formulas as anomalies. We learn an ensemble of STL formulas based on observed data, without any specific knowledge of the CPS being monitored. We propose an algorithm based on Grammar-Guided Genetic Programming (G3P) that learns the ensemble automatically in a single evolutionary run. We test the effectiveness of our data-driven proposal on two real-world datasets, finding that the proposed one-shot algorithm provides good detection performance.

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Section: Methodsmentioning

confidence: 99%