| Reference : Federated Learning For Cyber Security: SOC Collaboration For Malicious URL Detection |
| Scientific congresses, symposiums and conference proceedings : Paper published in a journal | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/44927 | |||
| Federated Learning For Cyber Security: SOC Collaboration For Malicious URL Detection | |
| English | |
Khramtsova, Ekaterina  [] | |
| Hammerschmidt, Christian [] | |
| Lagraa, Sofiane [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >] | |
| State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >] | |
| 2020 | |
| IEEE International Conference on Distributed Computing Systems (ICDCS) | |
| Yes | |
| International Workshop on Network Meets Intelligent Computations (NMIC) | |
| from 29-11-2020 to 01-12-2020 | |
| [en] cyber-security ; federated-learning ; machine- learning | |
| [en] Managed security service providers increasingly rely
on machine-learning methods to exceed traditional, signature- based threat detection and classification methods. As machine- learning often improves with more data available, smaller orga- nizations and clients find themselves at a disadvantage: Without the ability to share their data and others willing to collaborate, their machine-learned threat detection will perform worse than the same model in a larger organization. We show that Feder- ated Learning, i.e. collaborative learning without data sharing, successfully helps to overcome this problem. Our experiments focus on a common task in cyber security, the detection of unwanted URLs in network traffic seen by security-as-a-service providers. Our experiments show that i) Smaller participants benefit from larger participants ii) Participants seeing different types of malicious traffic can generalize better to unseen types of attacks, increasing performance by 8% to 15% on average, and up to 27% in the extreme case. iii) Participating in Federated training never harms the performance of the locally trained model. In our experiment modeling a security-as-a service setting, Federated Learning increased detection up to 30% for some participants in the scheme. This clearly shows that Federated Learning is a viable approach to address issues of data sharing in common cyber security settings. | |
| Researchers ; Professionals ; Students ; General public ; Others | |
| http://hdl.handle.net/10993/44927 |
| File(s) associated to this reference | ||||||||||||||
|
Fulltext file(s):
| ||||||||||||||
All documents in ORBilu are protected by a user license.
