Publications

@proceedings{Spata2024,
title = {Acoustic Side Channel Attack for Keystroke Splitting in the Wild},
author = {Massimo Orazio Spata, Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato},
editor = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/10796234},
doi = {10.1109/MetroXRAINE62247.2024.10796234},
isbn = {979-8-3503-7800-9},
year = {2024},
date = {2024-12-24},
booktitle = {2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {131-136},
abstract = {With the increasing prevalence of smart devices in our daily lives, concerns about security vulnerabilities have become more prominent. Acoustic Side Channel Attacks (ASCA) have emerged as a significant threat, exploiting sound emissions from devices to infer sensitive information such as keystrokes or conversations. We propose a novel approach leveraging deep learning techniques for audio segmentation in ASCA scenarios. Our method involves preprocessing the audio data, extracting relevant features, and applying a Temporal Convolutional Network (TCN) model for keystroke classification. We conducted experiments on a diverse dataset comprising various types of smart devices and attack scenarios. Our experiments demonstrate the effectiveness of the proposed audio segmentation method as fundamental preparatory step in ASCA attacks. The segmentation model achieved high precision, sensitivity, and specificity values, indicating its robustness in accurately. Furthermore, we observed consistent performance across different types of smart devices and attack scenarios, highlighting the generalizability of our approach in real conditions. The high precision, sensitivity, and specificity values obtained in our evaluation underscore the reliability and practical utility of the proposed approach. Experimental results demonstrate a peak accuracy of 990%, showcasing the effectiveness and precision of the approach. This high level of accuracy underscores the reliability and potential real-world applicability of the findings for keystroke splitting in the wild.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}

@proceedings{Battiato2024,
title = {A Novel Pipeline Based on Attention Mechanism Within a GNN to Predict New Edges Between Diseases and Genes},
author = {Massimo Orazio Spata; Sabrina Conoci; Alessandro Ortis; Sebastiano Battiato},
editor = {IEEE},
url = {https://ieeexplore.ieee.org/abstract/document/10796979},
doi = {10.1109/MetroXRAINE62247.2024.10796979},
isbn = {979-8-3503-7800-9},
year = {2024},
date = {2024-12-24},
booktitle = {2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {565-569},
abstract = {In recent years, Graph Neural Networks (GNNs) have shown great success in predicting missing edges in complex networks such as social networks, protein-protein interaction networks, and knowledge graphs, among others. One of the areas where GNNs may be applied is in predicting new edges between diseases and genes. The relationship between diseases and genes is complex, with a single gene being associated with multiple diseases, and multiple genes being associated with a single disease. GNNs are well-suited to capture such complex relationships as they can incorporate the structural information of the graph along with node and edge features to make predictions. Nodes represent diseases and genes, whereas edges represent known associations between them. Node features can be defined based on various characteristics of diseases and genes, such as Gene Ontology (GO) terms, pathways, and gene expression profiles. Edge features can be defined based on the strength of the association be-tween diseases and genes. The presented research shows a novel pipeline which integrating attention mechanisms into GNNs enhancing model's ability to focus on relevant gene-disease associations, and improving prediction accuracy. Experimental results exhibit a peak accuracy of 80%, highlighting the effectiveness and precision of the approach. This notable accuracy emphasizes the reliability and real-world relevance of the findings for predicting new edges between diseases and genes.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}

@proceedings{Spata2024b,
title = {A New Deep Learning Pipeline for Acoustic Attack on Keyboards},
author = {Massimo Orazio Spata, , Valerio Maria Russo, Alessandro Ortis, Sebastiano Battiato},
editor = {Springer},
url = {https://link.springer.com/chapter/10.1007/978-3-031-66329-1_26#citeas},
doi = {https://doi.org/10.1007/978-3-031-66329-1_26},
isbn = {978-3-031-66328-4},
year = {2024},
date = {2024-07-31},
booktitle = {Lecture Notes in Networks and Systems ((LNNS,volume 1065))},
volume = {1065},
pages = {402-414},
abstract = {The increasing reliance on services based on recent Artificial Intelligence advancements has elevated concerns about security vulnerabilities, leading to the exploration of novel attack vectors such as keystroke acoustic attacks on keyboards. This research delves into a deep learning approach for such attacks, which exploits acoustic emissions produced during typing to infer sensitive information. Traditional methods of keystroke acoustic attacks have relied on hand-engineered features and shallow classifiers, often failing to capture the intricate patterns within the acoustic data. In contrast, deep learning models have demonstrated remarkable capabilities in learning intricate patterns from complex data sources. We propose the exploitation of a Temporal Convolutional Network (TCN) to process acoustic signals, providing a more sophisticated and adaptive approach for keystroke acoustic attack analysis. The employed deep learning model showcases superior performance in multiple dimensions achieving a peak validation accuracy of 98.3% for keystrokes recorded by phone, and 93.05% for keystrokes recorded via Zoom, obtaining the best performances with respect the related prior art.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}

@proceedings{Spata2024c,
title = {A Cross Platform Mobile Application for Indoor Navigation},
author = {Massimo Orazio Spata, Luigi Priano, Alessandro Ortis, Sebastiano Battiato},
editor = {Springer},
url = {https://link.springer.com/chapter/10.1007/978-3-031-66329-1_6},
doi = {https://doi.org/10.1007/978-3-031-66329-1_6},
isbn = {978-3-031-66328-4},
year = {2024},
date = {2024-07-31},
volume = {1065},
abstract = {The work presented in this paper aims to explore the use of Augmented Reality framework technology to develop an indoor navigation through mobile application for smartphones, which allows developers to create augmented reality experiences on mobile devices. The mobile application exploits the device’s camera and sensors to track the user’s location and to provide real-time directions through an augmented reality interface. It also incorporates machine learning algorithms to improve the accuracy of inferences and to provide personalized recommendations based on the user's preferences and behavior. The research focuses on comparison of different technical frameworks aspects of the developing mobile application, including the integration of Augmented Reality frameworks, machine learning algorithms, and navigation features. The mobile application has been tested in a real-world environment to evaluate its effectiveness and usability. The results of this research will contribute to the development of new technologies for indoor navigation and provide insights into the potential of Augmented Reality frameworks for creating immersive and interactive experiences.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}