FOSS: Towards fine-grained unknown class detection against the open-set attack spectrum with variable legitimate traffic

• Main Authors: ZHAO, Ziming, LI, Zhaoxuan, XIE, Xiaofei, YU, Jiongchi, ZHANG, Fan, ZHANG, Rui, CHEN, Binbin, LUO, Xiangyang, HU, Ming, MA, Wenrui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Intrusion detection system; fine-grained unknown class detection; isolation forest; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9363; https://ink.library.smu.edu.sg/context/sis_research/article/10363/viewcontent/FOSS_2024_av.pdf
• Institution: Singapore Management University

Anomaly-based network intrusion detection systems (NIDSs) are essential for ensuring cybersecurity. However, the security communities realize some limitations when they put most existing proposals into practice. The challenges are mainly concerned with (i) fine-grained unknown attack detection and (...