Massimo Guarascio

Luca Caviglione

Angelica Liguori

Proceedings of the 19th International Conference on Availability, Reliability and Security, 2024

2023

A federated approach for detecting data hidden in icons of mobile applications delivered via web and multiple stores.

[BibT_eX]

[DOI]

Soc. Netw. Anal. Min., December, 2023

A Deep Anomaly Detection System for IoT-Based Smart Buildings.

[BibT_eX]

[DOI]

Sensors, December, 2023

Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels.

[BibT_eX]

[DOI]

Theory Pract. Log. Program., July, 2023

Learning ensembles of deep neural networks for extreme rainfall event detection.

[BibT_eX]

[DOI]

Neural Comput. Appl., May, 2023

An Explainable Deep Ensemble Framework for Intelligent Ticket Management.

[BibT_eX]

[DOI]

ERCIM News, 2023

ORISHA: Improving Threat Detection through Orchestrated Information Sharing (Discussion Paper).

[BibT_eX]

[DOI]

Marco Zuppelli

Proceedings of the 31st Symposium of Advanced Database Systems, 2023

Explainable Process Deviance Discovery with Data-Efficient Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the Numerical Computations: Theory and Algorithms, 2023

Towards Self-Supervised Cross-Domain Fake News Detection.

[BibT_eX]

[DOI]

Carmela Comito

Proceedings of the Italian Conference on Cyber Security (ITASEC 2023), 2023

Using AI to face covert attacks in IoT and softwarized scenarios: challenges and opportunities.

[BibT_eX]

[DOI]

Proceedings of the Italia Intelligenza Artificiale, 2023

Fighting Misinformation, Radicalization and Bias in Social Media.

[BibT_eX]

[DOI]

Proceedings of the Italia Intelligenza Artificiale, 2023

Exploiting Deep Learning and Explanation Methods for Movie Tag Prediction.

[BibT_eX]

[DOI]

Erica Coppolillo

Marco Minici

Proceedings of the International Database Engineered Applications Symposium Conference, 2023

Learning Deep Fake-News Detectors from Scarcely-Labelled News Corpora.

[BibT_eX]

[DOI]

Proceedings of the 25th International Conference on Enterprise Information Systems, 2023

Occupancy Prediction in Multi-Occupant IoT Environments Leveraging Federated Learning.

[BibT_eX]

[DOI]

Irfanullah Khan

Flávia Coimbra Delicato

Emilio Greco

Antonio Guerrieri

Giandomenico Spezzano

Proceedings of the IEEE Intl Conf on Dependable, 2023

2022

A Machine Learning Approach for Rainfall Estimation Integrating Heterogeneous Data Sources.

[BibT_eX]

[DOI]

Salvatore Gabriele

Antonio Procopio

Pietro Sabatino

IEEE Trans. Geosci. Remote. Sens., 2022

Detection of Steganographic Threats Targeting Digital Images in Heterogeneous Ecosystems Through Machine Learning.

[BibT_eX]

[DOI]

J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 2022

Boosting Cyber-Threat Intelligence via Collaborative Intrusion Detection.

[BibT_eX]

[DOI]

Nunziato Cassavia

Future Gener. Comput. Syst., 2022

Combining deep ensemble learning and explanation for intelligent ticket management.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2022

Semi-Supervised Discovery of DNN-Based Outcome Predictors from Scarcely-Labeled Process Logs.

[BibT_eX]

[DOI]

Bus. Inf. Syst. Eng., 2022

Towards Extreme Multi-Label Classification of Multimedia Content.

[BibT_eX]

[DOI]

Marco Minici

Proceedings of the 30th Italian Symposium on Advanced Database Systems, 2022

Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons.

[BibT_eX]

[DOI]

Proceedings of the Pervasive Knowledge and Collective Intelligence on Web and Social Media, 2022

Detection of Network Covert Channels in IoT Ecosystems Using Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022), 2022

Learning and Explanation of Extreme Multi-label Deep Classification Models for Media Content.

[BibT_eX]

[DOI]

Marco Minici

Erika De Francesco

Pasquale Lambardi

Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Combining Active Learning and Fast DNN Ensembles for Process Deviance Discovery.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Detecting DoS and DDoS Attacks through Sparse U-Net-like Autoencoders.

[BibT_eX]

[DOI]

Nunziato Cassavia

Proceedings of the 34th IEEE International Conference on Tools with Artificial Intelligence, 2022

Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A Short Review.

[BibT_eX]

[DOI]

Erica Coppolillo

Angelica Liguori

Proceedings of the Digital Sovereignty in Cyber Security: New Challenges in Future Vision, 2022

A Loosely-coupled Neural-symbolic approach to Compliance of Electric Panels.

[BibT_eX]

[DOI]

Proceedings of the 37th Italian Conference on Computational Logic, Bologna, Italy, June 29, 2022

Revealing MageCart-like Threats in Favicons via Artificial Intelligence.

[BibT_eX]

[DOI]

Proceedings of the ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna,Austria, August 23, 2022

2021

On learning effective ensembles of deep neural networks for intrusion detection.

[BibT_eX]

[DOI]

Inf. Fusion, 2021

Discovering accurate deep learning based predictive models for automatic customer support ticket classification.

[BibT_eX]

[DOI]

Proceedings of the SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, 2021

Sanitization of Images Containing Stegomalware via Machine Learning Approaches.

[BibT_eX]

[DOI]

Proceedings of the Italian Conference on Cybersecurity, 2021

2020

Using Deep Learning and Data Integration for Accurate Rainfall Estimates.

[BibT_eX]

[DOI]

Salvatore Gabriele

ERCIM News, 2020

Exploiting Temporal Convolution for Activity Prediction in Process Analytics.

[BibT_eX]

[DOI]

Proceedings of the ECML PKDD 2020 Workshops, 2020

A Multi-view Ensemble of Deep Models for the Detection of Deviant Process Instances.

[BibT_eX]

[DOI]

Proceedings of the ECML PKDD 2020 Workshops, 2020

A Deep Learning Approach for Detecting Security Attacks on Blockchain.

[BibT_eX]

[DOI]

Francesco Scicchitano

Proceedings of the Fourth Italian Conference on Cyber Security, 2020

Deep Autoencoder Ensembles for Anomaly Detection on Blockchain.

[BibT_eX]

[DOI]

Francesco Scicchitano

Proceedings of the Foundations of Intelligent Systems - 25th International Symposium, 2020

2019

Network Models.

[BibT_eX]

[DOI]

Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019

Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019

Knowledge Discovery in Databases.

[BibT_eX]

[DOI]

Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019

Network Topology.

[BibT_eX]

[DOI]

Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019

Deep Learning on Big Data.

[BibT_eX]

[DOI]

Maryam Amir Haeri

Proceedings of the Encyclopedia of Big Data Technologies., 2019

Predictive monitoring of temporally-aggregated performance indicators of business processes against low-level streaming events.

[BibT_eX]

[DOI]

Inf. Syst., 2019

Exploiting fractal dimension and a distributed evolutionary approach to classify data streams with concept drifts.

[BibT_eX]

[DOI]

Giuseppe Papuzzo

Appl. Soft Comput., 2019

Deep Sequential Modeling for Recommendation.

[BibT_eX]

[DOI]

Proceedings of the 27th Italian Symposium on Advanced Database Systems, 2019

A Deep Learning based architecture for rainfall estimation integrating heterogeneous data sources.

[BibT_eX]

[DOI]

Salvatore Gabriele

Proceedings of the International Joint Conference on Neural Networks, 2019

Learning Effective Neural Nets for Outcome Prediction from Partially Labelled Log Data.

[BibT_eX]

[DOI]

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

2018

A Predictive Learning Framework for Monitoring Aggregated Performance Indicators over Business Process Events.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Database Engineering & Applications Symposium, 2018

2017

A descriptive clustering approach to the analysis of quantitative business-process deviances.

[BibT_eX]

[DOI]

Proceedings of the Symposium on Applied Computing, 2017

Integrating a Framework for Discovering Alternative App Stores in a Mobile App Monitoring Platform.

[BibT_eX]

[DOI]

Proceedings of the New Frontiers in Mining Complex Patterns - 6th International Workshop, 2017

Deviance-Aware Discovery of High Quality Process Models.

[BibT_eX]

[DOI]

Proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence, 2017

Experimenting and Assessing a Probabilistic Business Process Deviance Mining Framework Based on Ensemble Learning.

[BibT_eX]

[DOI]

Proceedings of the Enterprise Information Systems - 19th International Conference, 2017

Extensions, Analysis and Experimental Assessment of a Probabilistic Ensemble-learning Framework for Detecting Deviances in Business Process Instances.

[BibT_eX]

[DOI]

Proceedings of the ICEIS 2017, 2017

2016

A Robust and Versatile Multi-View Learning Framework for the Detection of Deviant Business Process Instances.

[BibT_eX]

[DOI]

Int. J. Cooperative Inf. Syst., 2016

Profiling Human Behavior Through Multidimensional Latent Factor Modeling.

[BibT_eX]

[DOI]

Pietro Sabatino

Proceedings of the New Frontiers in Mining Complex Patterns - 5th International Workshop, 2016

A multi-view multi-dimensional ensemble learning approach to mining business process deviances.

[BibT_eX]

[DOI]

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

A Cloud-Based Prediction Framework for Analyzing Business Process Performances.

[BibT_eX]

[DOI]

Proceedings of the Availability, Reliability, and Security in Information Systems, 2016

2015

A Multi-view Learning Approach to the Discovery of Deviant Process Instances.

[BibT_eX]

[DOI]

Proceedings of the On the Move to Meaningful Internet Systems: OTM 2015 Conferences, 2015

On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-structured Business Processes.

[BibT_eX]

[DOI]

Proceedings of the ICEIS 2015, 2015

A Prediction Framework for Proactively Monitoring Aggregate Process-Performance Indicators.

[BibT_eX]

[DOI]

Proceedings of the 19th IEEE International Enterprise Distributed Object Computing Conference, 2015

Mining Multi-variant Process Models from Low-Level Logs.

[BibT_eX]

[DOI]

Proceedings of the Business Information Systems - 18th International Conference, 2015

2014

An Approach to the Discovery of Accurate and Expressive Fix-Time Prediction Models.

[BibT_eX]

[DOI]

Proceedings of the Enterprise Information Systems - 16th International Conference, 2014

A Framework for the Discovery of Predictive Fix-time Models.

[BibT_eX]

[DOI]

Proceedings of the ICEIS 2014, 2014

Mining Predictive Process Models out of Low-level Multidimensional Logs.

[BibT_eX]

[DOI]

Proceedings of the Advanced Information Systems Engineering, 2014

2013

Adaptive Trace Abstraction Approach for Predicting Business Process Performances.

[BibT_eX]

Proceedings of the 21st Italian Symposium on Advanced Database Systems, 2013

Discovering High-Level Performance Models for Ticket Resolution Processes.

[BibT_eX]

[DOI]

Proceedings of the On the Move to Meaningful Internet Systems: OTM 2013 Conferences, 2013

A Data-Driven Prediction Framework for Analyzing and Monitoring Business Process Performances.

[BibT_eX]

[DOI]

Proceedings of the Enterprise Information Systems - 15th International Conference, 2013

A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances.

[BibT_eX]

[DOI]

Proceedings of the ICEIS 2013, 2013

2012

Context-Aware Predictions on Business Processes: An Ensemble-Based Solution.

[BibT_eX]

[DOI]

Proceedings of the New Frontiers in Mining Complex Patterns - First International Workshop, 2012

Discovering Context-Aware Models for Predicting Business Process Performances.

[BibT_eX]

[DOI]

Proceedings of the On the Move to Meaningful Internet Systems: OTM 2012, 2012

ProMetheuS: A Suite for Process Mining Applications.

[BibT_eX]

[DOI]

Proceedings of the CAiSE'12 Forum at the 24<sup>th</sup> International Conference on Advanced Information Systems Engineering (CAiSE), 2012

2010

Mining models of exceptional objects through rule learning.

[BibT_eX]

[DOI]

Proceedings of the 2010 ACM Symposium on Applied Computing (SAC), 2010

An Empirical Comparison of Collaborative Filtering Approaches on Netflix Data.

[BibT_eX]

[DOI]

Nicola Barbieri

Proceedings of the IIR 2010, 2010

A Block Mixture Model for Pattern Discovery in Preference Data.

[BibT_eX]

[DOI]

Nicola Barbieri