TY - GEN
T1 - Demo
T2 - 31st ACM SIGSAC Conference on Computer and Communications Security, CCS 2024
AU - Ayoubi, Solayman
AU - Blanc, Gregory
AU - Tixeuil, Sébastien
AU - Jmila, Houda
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/12/9
Y1 - 2024/12/9
N2 - Network-based Intrusion Detection Systems (NIDS) are crucial in cybersecurity, but evaluation methodologies are outdated and lack standardization, resulting in incomplete and unreliable assessments. To address these issues, we first proposed a comprehensive evaluation framework for Machine Learning-based Intrusion Detection Systems [1]. This framework accounts for the unique aspects, strengths, and weaknesses of ML algorithms. However, the initial proposition lacked practicality, as it presented an abstract methodology without a substantive solution. In this paper, we present a demo of FREIDA a precise and concrete implementation of our framework, featuring an easy-to-use graphical interface. We also outline FREIDA’s evaluation methodology and demonstrate its application in evaluating IDS using a dataset from the literature.
AB - Network-based Intrusion Detection Systems (NIDS) are crucial in cybersecurity, but evaluation methodologies are outdated and lack standardization, resulting in incomplete and unreliable assessments. To address these issues, we first proposed a comprehensive evaluation framework for Machine Learning-based Intrusion Detection Systems [1]. This framework accounts for the unique aspects, strengths, and weaknesses of ML algorithms. However, the initial proposition lacked practicality, as it presented an abstract methodology without a substantive solution. In this paper, we present a demo of FREIDA a precise and concrete implementation of our framework, featuring an easy-to-use graphical interface. We also outline FREIDA’s evaluation methodology and demonstrate its application in evaluating IDS using a dataset from the literature.
KW - Data-driven Evaluation
KW - Evaluation Tool
KW - Intrusion Detection System
KW - Machine learning
UR - https://www.scopus.com/pages/publications/85215514433
U2 - 10.1145/3658644.3691368
DO - 10.1145/3658644.3691368
M3 - Conference contribution
AN - SCOPUS:85215514433
T3 - CCS 2024 - Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security
SP - 5081
EP - 5083
BT - CCS 2024 - Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security
PB - Association for Computing Machinery, Inc
Y2 - 14 October 2024 through 18 October 2024
ER -