Motif discovery based data discretization to improve interpretability of timed automata

As industries continue to increasingly rely on technology and automation to carry out their routine operations, the number of cyber attacks targeting industrial enterprises likewise is growing. Usually when an attack takes place, a change in the behaviour of the system is observed. These changes to a standard system behaviour are known as...

Real-time Intrusion Detection of Cyber Physical Systems

Intrusion detection problem in Industrial Control Systems(ICS), such as water treat- ment plant and power grid, is an important real-world problem. Real-time anomaly detection have been proposed to minimize the risk of cyber attack. In this study, two different kind of intrusion detection mode-based approach are learned from normal behaviour of...

Scheduling Strategies for Event-Triggered Control Using Timed Game Automata Over CAN Networks

Modern times have seen an increasing use of networked control systems, in which plants and controllers may not necessarily have a direct link but instead be connected through a network, thereby closing control loops over multiple nodes.<br/><br/>The system may also be spread out spatially over a large area, and thus the associated network delays...

Automating scheduler design for Networked Control Systems with Event-Based Control: An approach with Timed Automata

As the use of Networked Control Systems increases, the need for control methods with more efficient network usage also grows. These methods require a more sophisticated way of pre- dicting their traffic, and an approach for this is using a formal modelling approach using Timed Automata. Timed Automata have been used for over 25 years for several...

Abstracted models for scheduling of event-triggered control data traffic

Event-Triggered control (ETC) implementations have been proposed to overcome the inefficiencies of periodic (time-triggered) controller designs, namely the over-exploitation of the computing and communication infrastructure. However, the potential of aperiodic Event-Triggered techniques to reuse the freed bandwidth, and to reduce energy...

Car-following Behavior Model Learning Using Timed Automata

Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common state sequences are extracted from the model...

Traffic Characterization of Aperiodic Control Systems

In networked systems, particularly over wireless or shared channels, the scarcity of communication resources makes the application of traditional control strategies with periodic sampling problematic. Alternative approaches with aperiodic sampling, such as: event triggered control and self triggered control, have been recently proposed to reduce...

Efficiently identifying deterministic real-time automata from labeled data

We develop a novel learning algorithm RTI for identifying a deterministic real-time automaton (DRTA) from labeled time-stamped event sequences. The RTI algorithm is based on the current state of the art in deterministic finite-state automaton (DFA) identification, called evidence-driven state-merging (EDSM). In addition to having a DFA structure...

The efficiency of identifying timed automata and the power of clocks

We develop theory on the efficiency of identifying (learning) timed automata. In particular, we show that: (i) deterministic timed automata cannot be identified efficiently in the limit from labeled data and (ii) that one-clock deterministic timed automata can be identified efficiently in the limit from labeled data. We prove these results based...

Efficient Identification of Timed Automata: Theory and practice

This thesis contains a study in a subfield of artificial intelligence, learning theory, machine learning, and statistics, known as system (or language) identification. System identification is concerned with constructing (mathematical) models from observations. Such a model is an intuitive description of a complex system. One of the main nice...