Analyzing Microblogging Posts for Tracking Collective Emotional Trajectories. - Corrado Loglisci; Giuseppina Andresini; Angelo Impedovo; Donato Malerba (AI*IA, 2018)

Exploiting the Auto-Encoder Residual Error for Intrusion Detection. - Giuseppina Andresini; Annalisa Appice; Nicola Di Mauro; Corrado Loglisci; Donato Malerba (EuroS&P Workshops, 2019)

Multi-Channel Deep Feature Learning for Intrusion Detection. - Giuseppina Andresini; Annalisa Appice; Nicola Di Mauro; Corrado Loglisci; Donato Malerba (IEEE Access, 2020)

Clustering-Aided Multi-View Classification: A Case Study on Android Malware Detection. - Annalisa Appice; Giuseppina Andresini; Donato Malerba (J. Intell. Inf. Syst., 2020)

Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (Complex Pattern Mining, 2020)

Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images. - Giuseppina Andresini; Annalisa Appice; Domenico Dell'Olio; Donato Malerba (AI*IA, 2021)

INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection. - Giuseppina Andresini; Feargus Pendlebury; Fabio Pierazzi; Corrado Loglisci; Annalisa Appice; Lorenzo Cavallaro (AISec@CCS, 2021)

A Network Intrusion Detection System for Concept Drifting Network Traffic Data. - Giuseppina Andresini; Annalisa Appice; Corrado Loglisci; Vincenzo Belvedere; Domenico Redavid; Donato Malerba (DS, 2021)

Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems. - Francesco Paolo Caforio; Giuseppina Andresini; Gennaro Vessio; Annalisa Appice; Donato Malerba (DS, 2021)

A Two-Step Network Intrusion Detection System for Multi-Class Classification (Discussion Paper). - Giuseppina Andresini; Annalisa Appice; Donato Malerba (SEBD, 2021)

GAN augmentation to deal with imbalance in imaging-based intrusion detection. - Giuseppina Andresini; Annalisa Appice; Luca De Rose; Donato Malerba (Future Gener. Comput. Syst., 2021)

Autoencoder-based deep metric learning for network intrusion detection. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (Inf. Sci., 2021)

Nearest cluster-based intrusion detection through convolutional neural networks. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (Knowl. Based Syst., 2021)

An XAI-based adversarial training approach for cyber-threat detection. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (DASC/PiCom/CBDCom/CyberSciTech, 2022)

XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (ISMIS, 2022)

Review Spam Detection using Multi-View Deep Learning Combining Content and Behavioral Features. - Giuseppina Andresini; Andrea Iovine; Roberto Gasbarro; Marco Lomolino; Marco de Gemmis; Annalisa Appice (itaDATA, 2022)

ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection. - Giuseppina Andresini; Annalisa Appice; Francesco Paolo Caforio; Donato Malerba; Gennaro Vessio (Expert Syst. Appl., 2022)

Leveraging autoencoders in change vector analysis of optical satellite images. - Giuseppina Andresini; Annalisa Appice; Daniele Iaia; Donato Malerba; Nicolò Taggio; Antonello Aiello (J. Intell. Inf. Syst., 2022)

CENTAURO: An Explainable AI Approach for Customer Loyalty Prediction in Retail Sector. - Giuseppina Andresini; Annalisa Appice; Pasquale Ardimento; Andrea Antonio Brunetta; Antonio Giuseppe Doronzo; Giuseppe Ieva; Francesco Luce; Donato Malerba; Vincenzo Pasquadibisceglie (AI*IA, 2023)

GLORIA: A Graph Convolutional Network-Based Approach for Review Spam Detection. - Giuseppina Andresini; Annalisa Appice; Roberto Gasbarro; Donato Malerba (DS, 2023)

Striving for Simplicity in Deep Neural Models Trained for Malware Detection. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (PKDD/ECML Workshops (3), 2023)

Using Graph Neural Networks for the Detection and Explanation of Network Intrusions. - Ahmed Rafik El-Mehdi Baahmed; Giuseppina Andresini; Céline Robardet; Annalisa Appice (PKDD/ECML Workshops (3), 2023)

SENECA: Change detection in optical imagery using Siamese networks with Active-Transfer Learning. - Giuseppina Andresini; Annalisa Appice; Dino Ienco; Donato Malerba (Expert Syst. Appl., 2023)

$ mathsf{SILVIA}$: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023)

Editorial: AI meets cybersecurity. - Giuseppina Andresini; Annalisa Appice (J. Intell. Inf. Syst., 2023)

An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images. - Vito Recchia; Giuseppina Andresini; Annalisa Appice; Gianpietro Fontana; Donato Malerba (DS (2), 2024)

Potential of Spectral-Spatial Analysis to Map Forest Tree Dieback Due to Bark Beetle Hotspots in Sentinel-2 Images. - Giuseppina Andresini; Annalisa Appice; Dino Ienco; Donato Malerba; Vito Recchia (IGARSS, 2024)

Enhancing Cyber-threat detection coupling Deep Neural Ensemble Learning with XAI. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Ital-IA, 2024)

Leveraging Sentinel-2 time series for bark beetle-induced forest dieback inventory. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (SAC, 2024)

VINCENT: Cyber-threat detection through vision transformers and knowledge distillation. - Luca De Rose; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Comput. Secur., 2024)

A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data. - Giuseppina Andresini; Annalisa Appice; Donato Malerba (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)

PANACEA: a neural model ensemble for cyber-threat detection. - Malik Al-Essa; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Mach. Learn., 2024)

DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images. - Giuseppina Andresini; Annalisa Appice; Dino Ienco; Vito Recchia (J. Intell. Inf. Syst., 2024)

Adversarial Training to Improve Accuracy and Robustness of a Windows PE Malware Detection Model. - Luca Lobascio; Giuseppina Andresini; Annalisa Appice; Donato Malerba (ITASEC/SERICS, 2025)

OLIVANDER: a counterfactual-based method to generate adversarial Windows PE malware. - Luca De Rose; Giuseppina Andresini; Annalisa Appice; Donato Malerba (Data Min. Knowl. Discov., 2025)