The research objective of this study is to propose and develop a novel Federated Learning framework, known as SentinelNet, specifically tailored to address the security challenges present in decentralized environments. As decentralized networks continue to grow in popularity and significance across various domains, the need for robust and effective security measures becomes increasingly vital. However, traditional security approaches designed for centralized systems may not be well-suited to tackle the unique security challenges posed by decentralized networks.
The primary aim of the research is to leverage the power of collaborative machine learning, specifically through the innovative application of Federated Learning, to enhance network security while also respecting data privacy. In the context of decen- tralized environments, where data is distributed across multiple nodes or devices, it is often impractical or undesirable to centralize data collection and analysis due to privacy concerns and the decentralized nature of data sources.
By adopting a collaborative approach, the research seeks to bring together the collective knowledge and resources of decentralized network participants. This col- laborative effort enables a more comprehensive understanding of the ever-evolving threat landscape, facilitating the development of more effective and resilient security measures.
The research recognizes Federated Learning as a powerful solution to address the security challenges in decentralized environments. Instead of sending data to a central server for analysis, Federated Learning allows each node to locally train a shared security model using its own data. Only the model updates, rather than raw data, are exchanged during the training process, ensuring that sensitive information remains on the individual nodes and privacy is preserved.
Through the development of SentinelNet, the research aims to extend the appli- cation of Federated Learning to the domain of network security. The framework is designed to revolutionize collaborative security efforts by enabling nodes in decentral- ized environments to jointly improve their security defenses without compromising data privacy.
To achieve the research objective, the study outlines the architecture and work- flow of SentinelNet, incorporating essential components such as decentralized nodes, secure communication protocols, encryption techniques, and privacy-preserving mech- anisms. Additionally, the research evaluates the performance of SentinelNet using various metrics and real-world case studies to demonstrate its efficacy in enhancing network security while preserving data confidentiality.
Overall, the research objective seeks to contribute to the advancement of network security in decentralized environments by introducing SentinelNet as a pioneering Fed- erated Learning framework. By doing so, it aims to encourage the wider adoption of collaborative approaches to network security and promote the application of Feder- ated Learning as a viable solution in safeguarding the future of decentralized network ecosystems.