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@inproceedings{2269259,
author = {Macák, Martin and Rebok, Tomáš and Štovčik, Matúš and Ge, Mouzhi and Rossi, Bruno and Bühnová, Barbora},
address = {Setubal, Portugal},
booktitle = {Proceedings of the 8th International Conference on Internet of Things, Big Data and Security IoTBDS - Volume 1},
doi = {http://dx.doi.org/10.5220/0011929000003482},
keywords = {Network Security; Network Traffic Analysis; Forensics Analysis; Big Data; Insider Attack Detection},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Setubal, Portugal},
isbn = {978-989-758-643-9},
pages = {150-161},
publisher = {SciTePress},
title = {CopAS: A Big Data Forensic Analytics System},
url = {https://www.scitepress.org/PublicationsDetail.aspx?ID=umluZcUjShA=&t=1},
year = {2023}
}
TY - JOUR
ID - 2269259
AU - Macák, Martin - Rebok, Tomáš - Štovčik, Matúš - Ge, Mouzhi - Rossi, Bruno - Bühnová, Barbora
PY - 2023
TI - CopAS: A Big Data Forensic Analytics System
PB - SciTePress
CY - Setubal, Portugal
SN - 9789897586439
KW - Network Security
KW - Network Traffic Analysis
KW - Forensics Analysis
KW - Big Data
KW - Insider Attack Detection
UR - https://www.scitepress.org/PublicationsDetail.aspx?ID=umluZcUjShA=&t=1
N2 - With the advancing digitization of our society, network security has become one of the critical concerns for most organizations. In this paper, we present CopAS, a system targeted at Big Data forensics analysis, allowing network operators to comfortably analyze and correlate large amounts of network data to get insights about potentially malicious and suspicious events. We demonstrate the practical usage of CopAS for insider attack detection on a publicly available PCAP dataset and show how the system can be used to detect insiders hiding their malicious activity in the large amounts of data streams generated during the operations of an organization within the network.
ER -
MACÁK, Martin, Tomáš REBOK, Matúš ŠTOVČIK, Mouzhi GE, Bruno ROSSI a Barbora BÜHNOVÁ. CopAS: A Big Data Forensic Analytics System. Online. In \textit{Proceedings of the 8th International Conference on Internet of Things, Big Data and Security IoTBDS - Volume 1}. Setubal, Portugal: SciTePress, 2023, s.~150-161. ISBN~978-989-758-643-9. Dostupné z: https://dx.doi.org/10.5220/0011929000003482.
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