Massimo Guarascio

Orcid: 0000-0001-7711-9833

Affiliations:
  • National Research Council, Italy
  • University of Calabria, Cosenza, Italy (PhD 2011)


According to our database1, Massimo Guarascio authored at least 83 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Movie tag prediction: An extreme multi-label multi-modal transformer-based solution with explanation.
J. Intell. Inf. Syst., August, 2024

Data- & compute-efficient deviance mining via active learning and fast ensembles.
J. Intell. Inf. Syst., August, 2024

Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems.
J. Intell. Inf. Syst., August, 2024

Towards Data- and Compute-Efficient Fake-News Detection: An Approach Combining Active Learning and Pre-Trained Language Models.
SN Comput. Sci., June, 2024

Dawn of LLM4Cyber: Current Solutions, Challenges, and New Perspectives in Harnessing LLMs for Cybersecurity.
Proceedings of the Ital-IA Intelligenza Artificiale, 2024

Erasing the Shadow: Sanitization of Images with Malicious Payloads Using Deep Autoencoders.
Proceedings of the Foundations of Intelligent Systems - 27th International Symposium, 2024

A Few to Unveil Them All: Leveraging Mixture of Experts on Minimal Data for Detecting Covert Channels in Containerized Cloud Infrastructures.
Proceedings of the IEEE European Symposium on Security and Privacy Workshops, 2024

No Country for Leaking Containers: Detecting Exfiltration of Secrets Through AI and Syscalls.
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.
Soc. Netw. Anal. Min., December, 2023

A Deep Anomaly Detection System for IoT-Based Smart Buildings.
Sensors, December, 2023

Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels.
Theory Pract. Log. Program., July, 2023

Learning ensembles of deep neural networks for extreme rainfall event detection.
Neural Comput. Appl., May, 2023

An Explainable Deep Ensemble Framework for Intelligent Ticket Management.
ERCIM News, 2023

ORISHA: Improving Threat Detection through Orchestrated Information Sharing (Discussion Paper).
Proceedings of the 31st Symposium of Advanced Database Systems, 2023

Towards Self-Supervised Cross-Domain Fake News Detection.
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.
Proceedings of the Italia Intelligenza Artificiale, 2023

Fighting Misinformation, Radicalization and Bias in Social Media.
Proceedings of the Italia Intelligenza Artificiale, 2023

Exploiting Deep Learning and Explanation Methods for Movie Tag Prediction.
Proceedings of the International Database Engineered Applications Symposium Conference, 2023

Learning Deep Fake-News Detectors from Scarcely-Labelled News Corpora.
Proceedings of the 25th International Conference on Enterprise Information Systems, 2023

Occupancy Prediction in Multi-Occupant IoT Environments Leveraging Federated Learning.
Proceedings of the IEEE Intl Conf on Dependable, 2023

2022
A Machine Learning Approach for Rainfall Estimation Integrating Heterogeneous Data Sources.
IEEE Trans. Geosci. Remote. Sens., 2022

Detection of Steganographic Threats Targeting Digital Images in Heterogeneous Ecosystems Through Machine Learning.
J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 2022

Boosting Cyber-Threat Intelligence via Collaborative Intrusion Detection.
Future Gener. Comput. Syst., 2022

Combining deep ensemble learning and explanation for intelligent ticket management.
Expert Syst. Appl., 2022

Semi-Supervised Discovery of DNN-Based Outcome Predictors from Scarcely-Labeled Process Logs.
Bus. Inf. Syst. Eng., 2022

Towards Extreme Multi-Label Classification of Multimedia Content.
Proceedings of the 30th Italian Symposium on Advanced Database Systems, 2022

Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons.
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.
Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022), 2022

Learning and Explanation of Extreme Multi-label Deep Classification Models for Media Content.
Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Combining Active Learning and Fast DNN Ensembles for Process Deviance Discovery.
Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems.
Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Detecting DoS and DDoS Attacks through Sparse U-Net-like Autoencoders.
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.
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.
Proceedings of the 37th Italian Conference on Computational Logic, Bologna, Italy, June 29, 2022

Revealing MageCart-like Threats in Favicons via Artificial Intelligence.
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.
Inf. Fusion, 2021

Discovering accurate deep learning based predictive models for automatic customer support ticket classification.
Proceedings of the SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, 2021

Sanitization of Images Containing Stegomalware via Machine Learning Approaches.
Proceedings of the Italian Conference on Cybersecurity, 2021

2020
Using Deep Learning and Data Integration for Accurate Rainfall Estimates.
ERCIM News, 2020

Exploiting Temporal Convolution for Activity Prediction in Process Analytics.
Proceedings of the ECML PKDD 2020 Workshops, 2020

A Multi-view Ensemble of Deep Models for the Detection of Deviant Process Instances.
Proceedings of the ECML PKDD 2020 Workshops, 2020

A Deep Learning Approach for Detecting Security Attacks on Blockchain.
Proceedings of the Fourth Italian Conference on Cyber Security, 2020

Deep Autoencoder Ensembles for Anomaly Detection on Blockchain.
Proceedings of the Foundations of Intelligent Systems - 25th International Symposium, 2020

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

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

Knowledge Discovery in Databases.
Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019

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

Deep Learning on Big Data.
Proceedings of the Encyclopedia of Big Data Technologies., 2019

Predictive monitoring of temporally-aggregated performance indicators of business processes against low-level streaming events.
Inf. Syst., 2019

Exploiting fractal dimension and a distributed evolutionary approach to classify data streams with concept drifts.
Appl. Soft Comput., 2019

Deep Sequential Modeling for Recommendation.
Proceedings of the 27th Italian Symposium on Advanced Database Systems, 2019

A Deep Learning based architecture for rainfall estimation integrating heterogeneous data sources.
Proceedings of the International Joint Conference on Neural Networks, 2019

Learning Effective Neural Nets for Outcome Prediction from Partially Labelled Log Data.
Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

2018
Deviance-Aware Discovery of High-Quality Process Models.
Int. J. Artif. Intell. Tools, 2018

A Predictive Learning Framework for Monitoring Aggregated Performance Indicators over Business Process Events.
Proceedings of the 22nd International Database Engineering & Applications Symposium, 2018

2017
A descriptive clustering approach to the analysis of quantitative business-process deviances.
Proceedings of the Symposium on Applied Computing, 2017

Integrating a Framework for Discovering Alternative App Stores in a Mobile App Monitoring Platform.
Proceedings of the New Frontiers in Mining Complex Patterns - 6th International Workshop, 2017

Experimenting and Assessing a Probabilistic Business Process Deviance Mining Framework Based on Ensemble Learning.
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.
Proceedings of the ICEIS 2017, 2017

2016
A Robust and Versatile Multi-View Learning Framework for the Detection of Deviant Business Process Instances.
Int. J. Cooperative Inf. Syst., 2016

Profiling Human Behavior Through Multidimensional Latent Factor Modeling.
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.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

A Cloud-Based Prediction Framework for Analyzing Business Process Performances.
Proceedings of the Availability, Reliability, and Security in Information Systems, 2016

2015
A Multi-view Learning Approach to the Discovery of Deviant Process Instances.
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.
Proceedings of the ICEIS 2015, 2015

A Prediction Framework for Proactively Monitoring Aggregate Process-Performance Indicators.
Proceedings of the 19th IEEE International Enterprise Distributed Object Computing Conference, 2015

Mining Multi-variant Process Models from Low-Level Logs.
Proceedings of the Business Information Systems - 18th International Conference, 2015

2014
An Approach to the Discovery of Accurate and Expressive Fix-Time Prediction Models.
Proceedings of the Enterprise Information Systems - 16th International Conference, 2014

A Framework for the Discovery of Predictive Fix-time Models.
Proceedings of the ICEIS 2014, 2014

Mining Predictive Process Models out of Low-level Multidimensional Logs.
Proceedings of the Advanced Information Systems Engineering, 2014

2013
Adaptive Trace Abstraction Approach for Predicting Business Process Performances.
Proceedings of the 21st Italian Symposium on Advanced Database Systems, 2013

Discovering High-Level Performance Models for Ticket Resolution Processes.
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.
Proceedings of the Enterprise Information Systems - 15th International Conference, 2013

A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances.
Proceedings of the ICEIS 2013, 2013

2012
Context-Aware Predictions on Business Processes: An Ensemble-Based Solution.
Proceedings of the New Frontiers in Mining Complex Patterns - First International Workshop, 2012

Discovering Context-Aware Models for Predicting Business Process Performances.
Proceedings of the On the Move to Meaningful Internet Systems: OTM 2012, 2012

ProMetheuS: A Suite for Process Mining Applications.
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.
Proceedings of the 2010 ACM Symposium on Applied Computing (SAC), 2010

An Empirical Comparison of Collaborative Filtering Approaches on Netflix Data.
Proceedings of the IIR 2010, 2010

A Block Mixture Model for Pattern Discovery in Preference Data.
Proceedings of the ICDMW 2010, 2010

2009
A Hierarchical Rule-based Framework for Accurate Classification in Imprecise Domains.
Proceedings of the Seventeenth Italian Symposium on Advanced Database Systems, 2009

High Quality True-Positive Prediction for Fiscal Fraud Detection.
Proceedings of the ICDM Workshops 2009, 2009

Rule Learning with Probabilistic Smoothing.
Proceedings of the Data Warehousing and Knowledge Discovery, 11th International Conference, 2009


  Loading...