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Automata Learning for Fast Detection of Anomalies in Network Traffic
Hošták, Viliam Samuel ; Matoušek, Petr (referee) ; Holík, Lukáš (advisor)
The focus of this thesis is the fast network anomaly detection based on automata learning. It describes and compares several chosen automata learning algorithms including their adaptation for the learning of network characteristics. In this work, various network anomaly detection methods based on learned automata are proposed which can detect sequential as well as statistical anomalies in target communication. For this purpose, they utilize automata's mechanisms, their transformations, and statistical analysis. Proposed detection methods were implemented and evaluated using network traffic of the protocol IEC 60870-5-104 which is commonly used in industrial control systems.
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Tool for Abstract Regular Model Checking
Chalk, Matěj ; Rogalewicz, Adam (referee) ; Hruška, Martin (advisor)
Formal verification methods offer a large potential to provide automated software correctness checking (based on sound mathematical roots), which is of vital importance. One such technique is abstract regular model checking, which encodes sets of reachable configurations and one-step transitions between them using finite automata and transducers, respectively. Though this method addresses problems that are undecidable in general, it facilitates termination in many practical cases, while also significantly reducing the state space explosion problem. This is achieved by accelerating the computation of reachability sets using incrementally refinable abstractions, while eliminating spurious counterexamples caused by overapproximation using a counterexample-guided abstraction refinement technique. The aim of this thesis is to create a well designed tool for abstract regular model checking, which has so far only been implemented in prototypes. The new tool will model systems using symbolic automata and transducers instead of their (less concise) classic alternatives.
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Library for Finite Automata and Transducers
Bieliková, Michaela ; Lengál, Ondřej (referee) ; Hruška, Martin (advisor)
Finite state automata are widely used in the field of computer science such as formal verification, system modelling, and natural language processing. However, the models representing the reality are complicated and can be defined upon big alphabets, or even infinite alphabets, and thus contain a lot of transitions. In these cases, using classical finite state automata is not very efficient. Symbolic automata are more concise by employing predicates as transition labels. Finite state transducers also have a wide range of application such as linguistics or formal verification. Symbolic transducers replace classic transition labels with two predicates, one for input symbols and one for output symbols. The goal of this thesis is to design a library for letter and symbolic automata and transducers which will be suitable for fast prototyping.
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Simulation for Symbolic Automata
Síč, Juraj ; Lengál, Ondřej (referee) ; Holík, Lukáš (advisor)
Symbolic automata are similar to classical automata with one big difference: transitions are labelled with predicates defined in separate logical theory. This allows usage of large alphabets while taking less space. In this work we are interested in computing simulation (a binary relation on states that language inclusion) for these automata. This can be then used for reducing the size of automata without the need to determinize them first. There exist few algorithms for computing simulation over Kripke structures, which were then altered to work over labeled transition systems and classical automata. We show how one of these algorithms can be modified for symbolic automata by using the partition of the alphabet domain that is compatible with the predicates labelling transitions and by using the possibilities of the alphabet theory.
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Automata Learning for Fast Detection of Anomalies in Network Traffic
Hošták, Viliam Samuel ; Matoušek, Petr (referee) ; Holík, Lukáš (advisor)
The focus of this thesis is the fast network anomaly detection based on automata learning. It describes and compares several chosen automata learning algorithms including their adaptation for the learning of network characteristics. In this work, various network anomaly detection methods based on learned automata are proposed which can detect sequential as well as statistical anomalies in target communication. For this purpose, they utilize automata's mechanisms, their transformations, and statistical analysis. Proposed detection methods were implemented and evaluated using network traffic of the protocol IEC 60870-5-104 which is commonly used in industrial control systems.
|
|
|
Tool for Abstract Regular Model Checking
Chalk, Matěj ; Rogalewicz, Adam (referee) ; Hruška, Martin (advisor)
Formal verification methods offer a large potential to provide automated software correctness checking (based on sound mathematical roots), which is of vital importance. One such technique is abstract regular model checking, which encodes sets of reachable configurations and one-step transitions between them using finite automata and transducers, respectively. Though this method addresses problems that are undecidable in general, it facilitates termination in many practical cases, while also significantly reducing the state space explosion problem. This is achieved by accelerating the computation of reachability sets using incrementally refinable abstractions, while eliminating spurious counterexamples caused by overapproximation using a counterexample-guided abstraction refinement technique. The aim of this thesis is to create a well designed tool for abstract regular model checking, which has so far only been implemented in prototypes. The new tool will model systems using symbolic automata and transducers instead of their (less concise) classic alternatives.
|
|
|
Library for Finite Automata and Transducers
Bieliková, Michaela ; Lengál, Ondřej (referee) ; Hruška, Martin (advisor)
Finite state automata are widely used in the field of computer science such as formal verification, system modelling, and natural language processing. However, the models representing the reality are complicated and can be defined upon big alphabets, or even infinite alphabets, and thus contain a lot of transitions. In these cases, using classical finite state automata is not very efficient. Symbolic automata are more concise by employing predicates as transition labels. Finite state transducers also have a wide range of application such as linguistics or formal verification. Symbolic transducers replace classic transition labels with two predicates, one for input symbols and one for output symbols. The goal of this thesis is to design a library for letter and symbolic automata and transducers which will be suitable for fast prototyping.
|
|
|
Simulation for Symbolic Automata
Síč, Juraj ; Lengál, Ondřej (referee) ; Holík, Lukáš (advisor)
Symbolic automata are similar to classical automata with one big difference: transitions are labelled with predicates defined in separate logical theory. This allows usage of large alphabets while taking less space. In this work we are interested in computing simulation (a binary relation on states that language inclusion) for these automata. This can be then used for reducing the size of automata without the need to determinize them first. There exist few algorithms for computing simulation over Kripke structures, which were then altered to work over labeled transition systems and classical automata. We show how one of these algorithms can be modified for symbolic automata by using the partition of the alphabet domain that is compatible with the predicates labelling transitions and by using the possibilities of the alphabet theory.
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