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Disambiguation of "Pawlicki, Marek"

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An Innovative Approach to Real-Time Concept Drift Detection in Network Security.

, , , , and . EIDWT, volume 193 of Lecture Notes on Data Engineering and Communications Technologies, page 130-139. Springer, (2024)

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Peter Pawlicki

Katja Marek

Rekonstruktion und Kulturgesellschaft. Uni Kassel, (2009)

Czeslaw Adalbert Pawlicki

Ji Marek

Kurt Marek

 

Other publications of authors with the same name

Network Intrusion Detection in the Wild - the Orange use case in the SIMARGL project., , , , , and . ARES, page 65:1-65:7. ACM, (2021)Towards Deployment Shift Inhibition Through Transfer Learning in Network Intrusion Detection., , and . ARES, page 58:1-58:6. ACM, (2022)Parameters Transfer Framework for Multi-domain Fake News Detection., , , and . MobiSec, volume 1644 of Communications in Computer and Information Science, page 85-96. Springer, (2022)The Methods and Approaches of Explainable Artificial Intelligence., , , and . ICCS (4), volume 12745 of Lecture Notes in Computer Science, page 3-17. Springer, (2021)How Explainable Is Explainability? Towards Better Metrics for Explainable AI., , , , and . RIIFORUM, page 685-695. Springer, (2023)The Proposition of Balanced and Explainable Surrogate Method for Network Intrusion Detection in Streamed Real Difficult Data., , , , and . ICCCI (CCIS Volume), volume 1463 of Communications in Computer and Information Science, page 241-252. Springer, (2021)The Identification and Creation of Ontologies for the Use in Law Enforcement AI Solutions - MAGNETO Platform Use Case., , , , , , , , , and 2 other author(s). ICCCI (2), volume 11684 of Lecture Notes in Computer Science, page 335-345. Springer, (2019)Real-time stream processing tool for detecting suspicious network patterns using machine learning., , , and . ARES, page 60:1-60:7. ACM, (2020)Towards Hybrid NIDS: Combining Rule-Based SIEM with AI-Based Intrusion Detectors., , , , and . ACR, volume 956 of Lecture Notes in Networks and Systems, page 244-255. Springer, (2024)The Feasibility of Deep Learning Use for Adversarial Model Extraction in the Cybersecurity Domain., , and . IDEAL (2), volume 11872 of Lecture Notes in Computer Science, page 353-360. Springer, (2019)