PSL is Dead. Long Live PSL
Kevin Smith, Hai Lin, Praveen Tiwari, Marjorie Sayer, Claudionor, Coelho

TL;DR
This paper extends Property Specification Language (PSL) to continuous domains by integrating machine learning with formal temporal models, enabling effective anomaly detection in real-time streaming data.
Contribution
It introduces a novel approach combining machine learning and PSL for continuous domain anomaly detection, with a practical implementation in the TEF monitoring package.
Findings
TEF accurately interprets temporal correlations between events.
The combined approach detects anomalies as deviations from learned distributions.
PSL can be effectively extended to continuous domains using machine learning.
Abstract
Property Specification Language (PSL) is a form of temporal logic that has been mainly used in discrete domains (e.g. formal hardware verification). In this paper, we show that by merging machine learning techniques with PSL monitors, we can extend PSL to work on continuous domains. We apply this technique in machine learning-based anomaly detection to analyze scenarios of real-time streaming events from continuous variables in order to detect abnormal behaviors of a system. By using machine learning with formal models, we leverage the strengths of both machine learning methods and formal semantics of time. On one hand, machine learning techniques can produce distributions on continuous variables, where abnormalities can be captured as deviations from the distributions. On the other hand, formal methods can characterize discrete temporal behaviors and relations that cannot be easily…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Formal Methods in Verification · Software System Performance and Reliability
