A flow-based IDS using Machine Learning in eBPF.
作者： Joachim Fabini , Tanja Zseby , Maximilian Bachl
DOI:
关键词: Kernel (statistics) 、 Network packet 、 Denial-of-service attack 、 Computer science 、 Linux kernel 、 Context (language use) 、 Process (computing) 、 Artificial intelligence 、 Machine learning 、 Flow network
摘要: … So far eBPF has been used for simple packet filtering applications such as firewalls or Denial of Service protection. We show that it is possible to develop a flow based network intrusion …
arxiv.org参考文章(6)
Iman Sharafaldin, Arash Habibi Lashkari, Ali A. Ghorbani, Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization international conference on information systems security. pp. 108- 116 ,(2018) , 10.5220/0006639801080116
Ido Ben-Yair, Pavel Rogovoy, Nezer Zaidenberg, AI & eBPF based performance anomaly detection system international conference on systems. pp. 180- 180 ,(2019) , 10.1145/3319647.3325842
YoungEun Choe, Jun-Sik Shin, Seunghyung Lee, JongWon Kim, eBPF/XDP Based Network Traffic Visualization and DoS Mitigation for Intelligent Service Protection International Conference on Emerging Internetworking, Data & Web Technologies. pp. 458- 468 ,(2020) , 10.1007/978-3-030-39746-3_47
Félix Iglesias, Daniel C. Ferreira, Gernot Vormayr, Maximilian Bachl, Tanja Zseby, NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research Applied Sciences. ,vol. 10, pp. 4307- ,(2020) , 10.3390/APP10124307
Alexander Hartl, Maximilian Bachl, Joachim Fabini, Tanja Zseby, Explainability and Adversarial Robustness for RNNs international conference on big data. pp. 148- 156 ,(2020) , 10.1109/BIGDATASERVICE49289.2020.00030