3,097 research outputs found
Detection of Smart Grid Integrity Attacks Using Signal Temporal Logic
Cyber-attacks can have severe impacts on critical infrastructures, from
outages to economical loss and physical damage to people and environment. One
of the main targets of these attacks is the smart grid. In this paper, we
propose a new software detector for integrity attacks targeting smart meter
readings. The detector relies upon mining parameters of temporal logic
specifications for integrity attack classification. To this end, we use Signal
Temporal Logic (STL) for specifying properties over time series. Our approach
considers different "attack scenarios" found in last years: given a parametric
formula for each "attack scenario" and a set of labeled traces, we aim at
finding the parameter valuation that validates each template.Comment: Editor: Geert Deconinck. 18th European Dependable Computing
Conference (EDCC 2022), September 12-15, 2022, Zaragoza, Spain. Fast Abstract
Proceedings - EDCC 202
An Efficient Formula Synthesis Method with Past Signal Temporal Logic
In this work, we propose a novel method to find temporal properties that lead
to the unexpected behaviors from labeled dataset. We express these properties
in past time Signal Temporal Logic (ptSTL). First, we present a novel approach
for finding parameters of a template ptSTL formula, which extends the results
on monotonicity based parameter synthesis. The proposed method optimizes a
given monotone criteria while bounding an error. Then, we employ the parameter
synthesis method in an iterative unguided formula synthesis framework. In
particular, we combine optimized formulas iteratively to describe the causes of
the labeled events while bounding the error. We illustrate the proposed
framework on two examples.Comment: 8 pages, 5 figures, conference pape
A robust genetic algorithm for learning temporal specifications from data
We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [9] and with a recently proposed decision-tree [8] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work
Are Formal Contracts a useful Digital Twin of Software Systems?
Digital Twins are a trend topic in the industry today to either manage runtime information or forecast properties of devices and products. The techniques for Digitial Twins are already employed in several disciplines of formal methods, in particular, formal verification, runtime verification and specification inference. In this paper, we connect the Digital Twin concept and existing research areas in the field of formal methods. We sketch how digital twins for software-centric systems can be forged from existing formal methods
- …