| Title | Federated Learning for Anomaly-Based Intrusion Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ayed, Mohamed Ali, Talhi, Chamseddine |
| Conference Name | 2021 International Symposium on Networks, Computers and Communications (ISNCC) |
| Keywords | Collaborative Work, composability, Data models, data privacy, Deep Learning, defense, Metrics, network intrusion detection, privacy, pubcrawl, resilience, Resiliency, telecommunication traffic, Zero day attacks |
| Abstract | We are attending a severe zero-day cyber attacks. Machine learning based anomaly detection is definitely the most efficient defence in depth approach. It consists to analyzing the network traffic in order to distinguish the normal behaviour from the abnormal one. This approach is usually implemented in a central server where all the network traffic is analyzed which can rise privacy issues. In fact, with the increasing adoption of Cloud infrastructures, it is important to reduce as much as possible the outsourcing of such sensitive information to the several network nodes. A better approach is to ask each node to analyze its own data and then to exchange its learning finding (model) with a coordinator. In this paper, we investigate the application of federated learning for network-based intrusion detection. Our experiment was conducted based on the C ICIDS2017 dataset. We present a f ederated learning on a deep learning algorithm C NN based on model averaging. It is a self-learning system for detecting anomalies caused by malicious adversaries without human intervention and can cope with new and unknown attacks without decreasing performance. These experimentation demonstrate that this approach is effective in detecting intrusion. |
| DOI | 10.1109/ISNCC52172.2021.9615816 |
| Citation Key | ayed_federated_2021 |
Federated Learning for Anomaly-Based Intrusion Detection