ZADIG: A novel Extended Detection and Response System
This paper introduces ZADIG XDR, an innovative Extended Detection and Response system designed to enhance real-time anomaly detection, response, and prevention. Using advanced artificial intelligence and machine learning techniques, the system is able to evaluate anomalous events and predict their recurrence. ZADIG XDR's modular architecture allows for extensive customization, supporting targeted and effective protection. The system's efficient proprietary data ingestion pipeline, based on multiple tools such as Zeek, Kafka, Logstash, ElasticSearch, and a fork of LoudML: ZADIG AI, maintained by bitCorp, automates data collection, processing, and storage, ensuring seamless integration of multiple sources for in-depth security analysis. ZADIG XDR's robustness and flexibility in detecting anomalies and mitigating advanced cyber threats are then demonstrated through a simulation of multiple attack scenarios: Man-in-the-Middle, Denial of Service, and Scanning attacks.
Authors
- Simone Guarino (ORCID: https://orcid.org/0000-0001-8700-4749)
- Roberto Setola (ORCID: https://orcid.org/0000-0002-8792-2520)
- Luca Faramondi (ORCID: https://orcid.org/0000-0002-3750-2405)
- Martina Nobili
- Ernesto Del Prete
- Stefano Perone
- Luca Piccirillo
- Giovanni Patruno
- Annunziata Laurenda
Institutions
- Università Campus Bio-Medico (IT)
- Istituto Nazionale per l'Assicurazione Contro gli Infortuni sul Lavoro (IT)
Publication Details
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1109/csr61664.2024.10679420
- Citations
- 2
- Primary Topic
- Image Processing Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.64