TY - CHAP
T1 - Privacy Benchmarking of Intrusion Detection Sytems
AU - Ayoubi, Solayman
AU - Blanc, Gregory
AU - Jmila, Houda
AU - Tixeuil, Sébastien
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Network-based Intrusion Detection Systems (NIDS) are crucial in safeguarding network security, especially as cyber threats continue to evolve in complexity and scope. Despite significant advancements in IDS development, the evaluation of these systems remains inconsistent and often inadequate, particularly concerning their resilience to privacy attacks. This paper addresses this critical gap by introducing a systematic approach to assess the privacy vulnerabilities of IDS. We implement and integrate our evaluation method into the FREIDA [4, 5] tool, which is specifically designed to ensure the completeness, reliability, and reproducibility of machine learning-based IDS evaluations. To validate our approach, we conduct extensive experiments using established datasets, demonstrating the effectiveness and reliability of our evaluation methodology.
AB - Network-based Intrusion Detection Systems (NIDS) are crucial in safeguarding network security, especially as cyber threats continue to evolve in complexity and scope. Despite significant advancements in IDS development, the evaluation of these systems remains inconsistent and often inadequate, particularly concerning their resilience to privacy attacks. This paper addresses this critical gap by introducing a systematic approach to assess the privacy vulnerabilities of IDS. We implement and integrate our evaluation method into the FREIDA [4, 5] tool, which is specifically designed to ensure the completeness, reliability, and reproducibility of machine learning-based IDS evaluations. To validate our approach, we conduct extensive experiments using established datasets, demonstrating the effectiveness and reliability of our evaluation methodology.
KW - Intrusion detection system
KW - Machine learning
KW - Membership inference
KW - Model extraction
KW - Privacy leaks
UR - https://www.scopus.com/pages/publications/105003957235
U2 - 10.1007/978-3-031-87772-8_34
DO - 10.1007/978-3-031-87772-8_34
M3 - Chapter
AN - SCOPUS:105003957235
T3 - Lecture Notes on Data Engineering and Communications Technologies
SP - 406
EP - 417
BT - Lecture Notes on Data Engineering and Communications Technologies
PB - Springer Science and Business Media Deutschland GmbH
ER -