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Database-assisted state machine learning

Walinga, Hielke (author)

The behavior of software systems can be modeled as state machines by looking at the log data from these systems. Conventional algorithms, such as L∗, however, require too much memory to process log data when it gets too large. These algorithms must first load all available data into memory, which is often way too much.<br/>The approach...

master thesis 2024

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SAD: State machine-based Anomaly Detection in User Behavior

Nguyen, Charlie (author)

Over the past centuries, cybercrime has constantly grown. Among the most popular attacks against companies are phishing emails that especially gained popularity for threat actors to use as a tool during the COVID-19 pandemic. By changing the working environment, most communication channels between employees shifted from personal conversations...

master thesis 2022

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Robust Attack Graphs

Mouwen, Dennis (author)

Every day, Intrusion Detection Systems around the world generate huge amounts of data. This data can be used to learn attacker behaviour, such as Techniques, Tactics, and Procedures (TTPs). Attack Graphs (AGs) provide a visual way of describing these attack patterns. They can be generated without expert knowledge and vulnerability reports. The...

master thesis 2022

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Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster

Cao, C.S. (author), Blaise, Agathe (author), Verwer, S.E. (author), Rebecchi, Filippo (author)

These days more companies are shifting towards using cloud environments to provide their services to their client. While it is easy to set up a cloud environment, it is equally important to monitor the system's runtime behaviour and identify anomalous behaviours that occur during its operation. In recent years, the utilisation of Recurrent...

conference paper 2022

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Modeling System Behaviour from Log Analysis Using Meta-Heuristic Search

Georgescu, Calin (author)

Understanding the behaviour of a software system plays a key role in its development and maintenance processes. Unfortunately, accurate and concise models are not always available during development, due to the rapid changes in the structure and scale systems may undergo during this phase. Finite State Machines (FSM) are a natural and prevalent...

bachelor thesis 2021

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Mining Attack Strategy: Using Process Mining to extract attacker strategy from IDS alerts

Habben Jansen, Geert (author)

Ever since the invention of the Internet, more and more computers are connected throughout the world. Though this has brought numerous new inventions used every day, like social media, e-commerce, and video conferencing, it also opens up new opportunities for cyber criminals. As the intrusion detection systems used to identify malicious behavior...

master thesis 2021

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Exploiting structure in counterexamples to speed up equivalence checking in the minimally adequate teacher framework: Active Learning

Catshoek, Tom (author)

Active state machine learning algorithms are a class of algorithms that allow us to infer state machines representing certain systems. These algorithms interact with a system and build a hypothesis of what the state machine describing that system looks like according to the behavior they observed. Once the algorithm arrives at a hypothesis, it...

master thesis 2021

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Threat Adaptive Byzantine Fault Tolerant State-Machine Replication

Simoes Silva, Douglas (author), Graczyk, Rafal (author), Decouchant, Jérémie (author), Völp, Marcus (author), Esteves-Veríssimo, Paulo (author)

Critical infrastructures have to withstand advanced and persistent threats, which can be addressed using Byzantine fault tolerant state-machine replication (BFT-SMR). In practice, unattended cyberdefense systems rely on threat level detectors that synchronously inform them of changing threat levels. However, to have a BFT-SMR protocol operate...

conference paper 2021

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Learning State Machines in Real-Time on a Small Dedicated Hardware Device

Cao, Clinton (author)

The Internet is a technology that was invented in the 1960s and was used only by a few users to do simple communications between computers. Fast forward to 2020, the Internet has become a technology that is being used by billions of users. It allows users to communicate with each other across the world and even allows users to access data...

master thesis 2020

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Learning State Machines faster using Locality-Sensitive Hashing and an application in network-based threat detection

Skoulos, R. (author)

The internet traffic is constantly rising nowadays due to the significant increase of the devices connected to the Internet. As a consequence, many cyber risks have arisen. Cybercriminals are trying to exploit the vulnerabilities of these devices to cause damage and gain profit. Monitoring the network traffic and detecting such threats has...

master thesis 2020

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Anomaly Detection in Network Traffic using Multivariate State Machines

Serentellos, V. (author)

Computer networks have nowadays assumed an increasingly important role in the expression of modern human activity through the ongoing rapid development in the field of Information and Communication Technologies (ICT). More and more individual users and businesses around the world are gaining access to networks online, while the range of services...

master thesis 2020

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Automatic modular design of robot swarms using behavior trees as a control architecture

Ligot, Antoine (author), Kuckling, Jonas (author), Bozhinoski, D. (author), Birattari, Mauro (author)

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules—low-level behaviors and conditions— into a behavior tree that...

journal article 2020

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Uncomputability in Information Theory

Bassem Tarek Abdelraheem Elsayed Safieldeen, Bassem (author)

We present a powerful approach for learning about uncomputability and undecidability in informationtheory. Our approach is to use automata from automata theory that have undecidable properties toconstruct channels for which an information-theoretic quantity is uncomputable or undecidable. Wedemonstrate this approach by showing that, for channels...

master thesis 2019

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Evolving State Machines as Robot Controllers

den Toom, Matthijs (author)

Automated generation of robot controllers using an Evolutionary Algorithm(EA) has received increasing attention in the last years as it has the potentialfor a reduction in the development time of a robot. Often these EAs generateNeural Networks (NNs) as robot controllers. Using a NN for automaticallygenerating robot controllers has two important...

master thesis 2019

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Log Differencing using State Machines for Anomaly Detection

Tsoni, Sofia (author)

Huge amounts of log data are generated every day by software. These data contain valuable information about the behavior and the health of the system, which is rarely exploited, because of their volume and unstructured nature. Manually going through log files is a time-consuming and labor-intensive procedure for developers. Nonetheless logging...

master thesis 2019

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Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices

MANGANAHALLI JAYAPRAKASH, Sandesh (author)

The usage of Internet of Things (IoT) devices has been exponentially increasing and their security is often overlooked. Hackers exploit the vulnerabilities present to perform large scale attacks as well as to obtain privacy-sensitive information. Resource constraints combined with a lack of incentives for manufacturers makes it harder to...

master thesis 2019

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Learning State Machines from data streams and an application in network-based threat detection

Schouten, Hans (author)

Our increasingly interconnected society poses large risks in terms of cyber security. With network traffic volumes increasing and systems becoming more connected, maintaining visibility on IT networks is a challenging yet important task. In recent years the number of cyber threats have increased dramatically. Monitoring and threat detection are...

master thesis 2018

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Testing Code Generators against Definitional Interpreters

Papadopoulos, Giannis (author)

Large companies suffer from the increasing complexity that exist in their software systems. Evolving their software becomes even harder if we consider that a change in one system can affect several other parts of their software architecture. Especially banks that need to be always complied with regulations, have to constantly make changes in...

master thesis 2018

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User Perceptions of a Humanoid Robot's Developing Behavior

Romi Kharisnawan, Romi (author)

An important challenge in developing a social robot is making the interaction between human and robot to be more pleasant and convenient. It could be obtained by making the robot to develop, i.e. change its behavior over the course of time. Here we study two aspects of development: behavioral adaptation and behavioral complexity to which we...

master thesis 2018

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Vulnerability Detection on Mobile Applications Using State Machine Inference

van der Lee, Wesley (author), Verwer, S.E. (author)

Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine...

conference paper 2018