In recent years, the importance of computer security has increased due to the rapid advancement of digital technology, widespread Internet use, and increased sophistication of cyberattacks. Machine learning has gained great interest in securing data systems because it offers the capability of automatically detecting and responding to security threats in real time, which is crucial for maintaining the security of computer systems and protecting data from malicious attacks. This study concentrates on phishing attack detection systems, a prevalent cyber-threat. These systems assess the features of the incoming requests to identify whether they are malicious or not. Although the number of features is increasing in these systems, feature selection has become an essential pre-processing phase that identifies the most important features of a set of available features to prevent overfitting problems, improve model performance, reduce computational cost, and decrease training and execution time. Leveraging genetic algorithms, known for simulating natural selection to identify optimal solutions, we propose a novel feature selection method, based on genetic algorithms and locally optimized, that is applied to a URL-based phishing detection system with machine learning models. Our research demonstrates that the proposed technique offers a promising strategy for improving the performance of machine learning models.

There has been a burst of discussions about how to characterize and recognize online dark patterns — i.e., web design strategies that aim to steer user choices towards what favours service providers or third parties like advertisers rather than what is in the best interest of users. Dark patterns are common in cookie banners where they are used to influence users to accept being tracked for more purposes than a data protection by default principle would dictate. Despite all the discussions, an objective, transparent, and verifiable assessment of dark patterns’ qualities is still missing. We contribute to bridging this gap by studying several cookie processes, in particular their multi-layered information flow —that we represent as message sequence charts—, and by identifying a list of observable and measurable features that we believe can help describing the presence of dark patterns in digital consent flows. We propose thirty one of such properties that can be operationalised into metrics and therefore into objective procedures for the detection of dark patterns.