The rapid growth of online services has heightened concerns about user protection from cyber threats, particularly phishing, which poses significant risks to cyber-social security. To this end, we propose a novel tool for phishing detection called U-Proof. Our tool uses both state-of-the-art LLMs and traditional ML models to detect phishing websites. In particular, we evaluate the phishing detection capabilities of different LLMs and compare them with several ML models to analyze the impact of different model architectures on the identification of phishing websites. For a comprehensive experimental evaluation, we use a combination of public and custom datasets. These include active phishing websites from September 2024, as well as URLs from banks and postal services. Furthermore, the tool includes explanations to enhance user awareness of phishing tactics, supporting broader educational efforts to reduce risks.

The importance of exploiting profiling metadata, such as Relaxed Functional Dependencies (RFDs), to support advanced data processing tasks, continues to grow also due to the availability of algorithms capable of automatically extracting them from data. Nevertheless, in order to use this type of metadata in real-life contexts, it is also necessary to ensure their correct interpretation of their meaningfulness and their possible evolution over time. To this end, in this paper, we present a new tool that allows visual analysis and explainability of how discovery results evolve according to changes in the data. More specifically, it provides a comprehensive overview of the impact that data changes, by possibly analyzing in-depth affected dependencies and understanding motivations underlying their evolution through a textual explanation. The effectiveness of the proposed tool has been evaluated by conducting a user study, which highlighted RYAN’s capability to yield an intuitive visualization of RFD discovery results and to provide a clear explanation of the reasons that led to the evolution of RFDs.

Over the past decade, there have been numerous extensions to the definition of Functional Dependency (fd), culminating in the introduction of Relaxed Functional Dependency (rfd), offering more flexible constraints compared to traditionalfds. This increased flexibility makesrfds well-suited for exploring and profiling data in datasets with lower data quality. However, efficiently identifyingrfds within dynamic data sources presents a significant challenge, as it requires processing an entire dataset from scratch whenever modifications occur. To tackle this problem, incremental discovery algorithms have been defined, but they often suffer when the frequency and the size of batches of updates increase. This article presents a new algorithm, namelyD-IndiBits, relying on a new decentralized architecture to balance the workload that drives the incremental discovery process ofIndiBits, which is based on bitwise operators for computing attribute similarities. Experiments demonstrateD-IndiBits's effectiveness compared tofdandrfddiscovery algorithms on both static and dynamic real-world data. With batches of modifications of sizes 10 k and 100 k,D-IndiBitsis capable of updating the set ofrfds in a few seconds, whereas all other approaches often employ more than 3 hours.

One of the main challenges in data profiling is to efficiently extract metadata from dynamic information sources, by avoiding the processing of the whole dataset from scratch upon modifications. In this paper, we present IndiBits, an algorithm for discovering relaxed functional dependencies (RFDs for short), which represent data relationships relying on approximate matching paradigms. IndiBits is able to dynamically infer and update the RFDs holding on a dataset upon modification operations performed on it. It exploits a binary representation of data similarities, a new validation method, and specific search methods, to dynamically update the set of RFDs, based on previously holding RFDs and the type of modifications performed over data. Experimental results demonstrate the effectiveness of IndiBits on real-world datasets, even in comparison with FD and RFD discovery algorithms in both static and dynamic scenarios.

With the advent of Big Data there is an increasing necessity to incrementally mine information from data originating from sensors and other dynamic sources. Thus, it is necessary to devise algorithms capable of mining useful information upon possible evolutions of databases. Among these, there are certainly data profiling info, such as functional dependencies (fd for short), which are particularly useful for data integration and for assessing the quality of data. The incremental scenario requires the definition of search strategies and validation methods able to analyze only the portion of the dataset affected by the last changes. In this paper, we propose a new validation method, which exploits regular expressions and compressed data structures to efficiently verify whether a candidate fd holds on an updated version of the dataset. Experimental results demonstrate the effectiveness of the proposed method on real-world datasets adapted for incremental scenarios, also compared with a baseline incremental fd discovery algorithm.