Malik AL-Essa is a PhD student at the Department of Computer Science, University of Bari Aldo Moro. He graduated from the University of Salerno in 2019 in Business Innovation and Informatics MSc degree. His PhD Project is entitled 'Machine Learning for Cyber Threat Investigation and Cyber Defense'.

Research Interests

Cybersecurity, Explainable AI, Machine Learning, Deep Leaning, Intrusion Detection, and Big Data Analytics.

Malik AL-Essa
PhD
Phone: +39 080 5442203
em@il: malik.alessa @ uniba.it



Publications Legend

Journal Articles
Book
Conference or Workshop
Editorship
Collection
Other

Publications


2024
Malik Al-Essa, Giuseppina Andresini, Annalisa Appice, Donato Malerba:
Enhancing Cyber-threat detection coupling Deep Neural Ensemble Learning with XAI. Ital-IA: 182-187 (2024)
DOI: https://ceur-ws.org/Vol-3762/529.pdf
Malik Al-Essa, Giuseppina Andresini, Annalisa Appice, Donato Malerba:
PANACEA: a neural model ensemble for cyber-threat detection. Mach. Learn. 113(8): 5379-5422 (2024)
DOI: https://doi.org/10.1007/s10994-023-06470-2


2023
Malik Al-Essa, Giuseppina Andresini, Annalisa Appice, Donato Malerba:
Striving for Simplicity in Deep Neural Models Trained for Malware Detection. PKDD/ECML Workshops (3): 529-540 (2023)
DOI: https://doi.org/10.1007/978-3-031-74633-8_40
Malik Al-Essa, Giuseppina Andresini, Annalisa Appice, Donato Malerba:
PANACEA: A Neural Model Ensemble for Cyber-Threat Detection. DSAA: 1-2 (2023)
DOI: https://doi.org/10.1109/DSAA60987.2023.10302518


2022
Malik Al-Essa, Giuseppina Andresini, Annalisa Appice, Donato Malerba:
An XAI-based adversarial training approach for cyber-threat detection. DASC/PiCom/CBDCom/CyberSciTech: 1-8 (2022)
DOI: https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927842
Malik Al-Essa, Giuseppina Andresini, Annalisa Appice, Donato Malerba:
XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity. ISMIS: 117-126 (2022)
DOI: https://doi.org/10.1007/978-3-031-16564-1_12


2021
Malik Al-Essa, Annalisa Appice:
Dealing with Imbalanced Data in Multi-class Network Intrusion Detection Systems Using XGBoost. PKDD/ECML Workshops (2): 5-21 (2021)
DOI: https://doi.org/10.1007/978-3-030-93733-1_1