IoT devices generate and exchange large amounts of data daily, creating significant security and privacy challenges. Security testing, particularly using Machine Learning (ML), helps identify and classify potential malicious network traffic. Previous research has shown how ML can aid in designing security tests for IoT attacks. This ongoing paper introduces a search-based approach using Genetic Algorithms (GAs) to evolve detection rules and detect intrusion attacks. We build on existing GA methods for intrusion detection and compare them with leading ML models. We propose 17 detection rules and demonstrate that while GAs do not fully replace ML, they perform well with ample attack examples and enhance the usability and implementation of deterministic test cases by security testers.

Electric vehicles are seen as a sustainable alternative in the current fight against increasing pollution worldwide. However, they are still seeing a limited spread among drivers and one of the challenges is to deepen the knowledge about the users' perceptions of this innovative means of transport. The current paper presents the work done in this framework by creating two Discrete Choice Experiments. The first one investigates aspects related to car ownership characteristics and performances, whereas the second one focuses on the features of the charging infrastructure. We describe the whole process that guided us to the current final version of these experiments. This includes the preliminary stage of a literature review and a productive discussion with stakeholders in the project framework arriving at the spread of questionnaires in two different Pilots. The current work stresses the need for all these passages to propose reliable experiments that could get new insights on the users' perception of electric vehicles.