Introducing Privacy-Enhancing Technologies to Consortium Blockchains

Blockchain technology has revolutionized the way data is stored, managed, and shared across various industries. Its decentralized nature and immutability make it highly attractive in use cases that require transparency, integrity, and accountability. However, some applications demand confidentiality, necessitating the development of permissioned...

Behind the Botnet: Evaluating Avalanche's security controls using a reconstruction of its anatomy from forensic evidence

How did Avalanche, a botnet with an active lifetime of 8 years while serving 20+ malware families, ensure a smooth operation of business? Avalanche had the attention of security researchers and law enforcement, yet it managed to persevere for a long period of time.<br/>In this work, we answer this question by analyzing Avalanche’s security...

SSE Is Not As Secure As It Looks: New Attacks On Range Queries Using PQ-Trees And Auxiliary Information

In a world where more data gets uploaded to the cloud, it is essential that the data gets stored securely. For users to keep search functionality, searchable symmetric encryption has been developed. SSE works by a user sending a token representing a keyword (or a range), after which the server returns the documents that match the keyword (or...

Securing BGP Communities: Design of a new RPKI object to mitigate BGP Community Attacks

Research has shown that the Border Gateway Protocol (BGP) is vulnerable to a new attack that exploits the community attribute. These community attacks can influence BGP routing in unintended ways. Currently, there are no effective mitigations against these attacks which do not limit the normal usage of BGP communities or offer cryptographic...

Device Type Classification of Internet of Things Devices on Low-End Dedicated Hardware Devices

The uprise of the Internet of Things (IoT) has been a hot topic for several years. While many see these devices and think they bring ease to their lives, it is far from reality. Researchers found many privacy and security problems within these devices in the last years. The popularity of these devices causes many users to bring a device into...

System Call Argument Filtering for Interpreted Languages

Interpreted applications are often vulnerable to remote code execution attacks. To protect interpreted applications, we should reduce the tools available to the attackers. In this thesis, we investigate the possibilities for the automation of policy generation for interpreted applications in terms of system call arguments. These policies are...

In Search of Best Learning Curve Model

Learning curves have been used extensively to analyse learners' behaviour and practical tasks such as model selection, speeding up training and tuning models. Nonetheless, we still have a relatively limited understanding of the behaviour of learning curves themselves, in particular, whether there exists a parametric function that can best model...

Evolving a Search Procedure for Program Synthesis

In recent months, researchers developed several new search procedures to augment the process of program synthesis. While many of them performed better than their predecessors, the proposed solutions are still far from ideal. One possible way of overcoming the shortcomings of single search methods is employing genetic algorithms, which have been...

Evolving a Domain-Specific Language to Speed Up Program Synthesis

Program synthesis is used in various ways to automate repetitive tasks or to generate software automatically. Search-based program synthesis constitutes searching the space of candidate programs created from a given language. However, this form of program synthesis is very expensive in terms of computing power. By optimising the synthesiser’s...

Genetic Algorithm for Evolving an Objective Function of a Program Synthesizer

Program Synthesis is a challenging problem in Artificial Intelligence. An important element of a program synthesizer is the objective function that guides the combinatorial search for a program that satisfies a given user intent. Given multiple I/O example transformations that correspond to the intended behavior of the program, this function...

Different approaches to fitting and extrapolating the learning curve

Extrapolation of the learning curve provides an estimation of how much data is needed to achieve the desired performance. It can be beneficial when gathering data is complex, or computation resource is limited. One of the essential processes of learning curve extrapolation is curve fitting. This research first analyses the behaviour of existing...

To Tune or not to Tune: Hyperparameter Influence on the Learning Curve

A learning curve displays the measure of accuracy/error on test data of a machine learning algorithm trained on different amounts of training data. They can be modeled by parametric curve models that help predict accuracy improvement through curve extrapolation methods. However, these learning curves have only been mainly generated from default...

Evolving Design Patterns for Program Synthesis

Design pattern provide an abstraction that the pro- gram synthesis algorithm can use in order to find programs easier. However, coming up with them is difficult as they are domain-specific. This paper showcases a novel approach to creating design pat- terns through the means of genetic algorithms. Re- sults are showing that while in the robot...

Explain Strange Learning Curves in Machine Learning

The learning curve illustrates how the generalization performance of the learner evolves with more training data. It can predict the amount of data needed for decent accuracy and the highest achievable accuracy. However, the behavior of learning curves is not well understood. Many assume that the more training data provided, the better the...

GENERALIZE: A framework for evolving searching constraints for domain-specific languages in program synthesis

In this paper, we propose a method for eliciting constraints for arbitrary Domain-Specific Languages (DSL) in Program Synthesis search. We argue that we can successfully predict constraints using a form of attribute-based induction. We also provide a novel approach to constraint verification using genetic algorithms to optimize desired results....

Factors related to dataset that influence the shape of learning curves

Although there are many promising applications of a learning curve in machine learning, such as model selection, we still know very little about what factors influence their behaviours. The aim is to study the impact of the inherent characteristics of the datasets on the learning shapes, which are noise, discretized input and dimensionality. We...