Andrea Melis

Andrea Melis, Andrea Piroddi, Roberto Girau

Cognitive Radio (CR) systems, which dynamically adapt to changing spectrum environments, could benefit significantly from advancements in machine learning technologies. These systems can be enhanced in terms of spectral efficiency, robustness, and security through innovative approaches such as the use of Transformer models. This work investigates the application of Transformer models, specifically the GPT-2 architecture, to generate novel modulation schemes for wireless communications. By training a GPT-2 model on a dataset of existing modulation formulas, new modulation schemes has been created. These generated schemes are then compared to traditional methods using key performance metrics such as Signal-to-Noise Ratio (SNR) and Power Spectrum Density (PSD). The results show that Transformer-generated modulation schemes can achieve performance comparable to, and in some cases outperforming, traditional methods. This demonstrates that advanced CR systems could greatly benefit from the implementation of Transformer models, leading to more efficient, robust, and secure communication systems.

Davide Berardi, Franco Callegati, Andrea Melis et al.

Passwords should be easy to remember, yet expiration policies mandate their frequent change. Caught in the crossfire between these conflicting requirements, users often adopt creative methods to perform slight variations over time. While easily fooling the most basic checks for similarity, these schemes lead to a substantial decrease in actual security, because leaked passwords, albeit expired, can be effectively exploited as seeds for crackers. This work describes an approach based on Bloom filters to detect password similarity, which can be used to discourage password reuse habits. The proposed scheme intrinsically obfuscates the stored passwords to protect them in case of database leaks, and can be tuned to be resistant to common cryptanalytic techniques, making it suitable for usage on exposed systems.

Franco Callegati, Saverio Giallorenzo, Andrea Melis et al.

Mobility as a Service (MaaS) applies the everything-as-a-service paradigm of Cloud Computing to transportation: a MaaS provider offers to its users the dynamic composition of solutions of different travel agencies into a single, consistent interface. Traditionally, transits and data on mobility belong to a scattered plethora of operators. Thus, we argue that the economic model of MaaS is that of federations of providers, each trading its resources to coordinate multi-modal solutions for mobility. Such flexibility comes with many security and privacy concerns, of which insider threat is one of the most prominent. In this paper, we follow a tiered structure --- from individual operators to markets of federated MaaS providers --- to classify the potential threats of each tier and propose the appropriate countermeasures, in an effort to mitigate the problems.