SmartSecChain-SDN: A Blockchain-Integrated Intelligent Framework for Secure and Efficient Software-Defined Networks
Not on the map. Why?
Abstract (arXiv)
With more and more existing networks being transformed to Software-Defined Networking (SDN), they need to be more secure and demand smarter ways of traffic control. This work, SmartSecChain-SDN, is a platform that combines machine learning based intrusion detection, blockchain-based storage of logs, and application-awareness-based priority in SDN networks. To detect network intrusions in a real-time, precision and low-false positives setup, the framework utilizes the application of advanced machine learning algorithms, namely Random Forest, XGBoost, CatBoost, and CNN-BiLSTM. SmartSecChain-SDN is based on the Hyperledger Fabric, which is a permissioned blockchain technology, to provide secure, scalable, and privacy-preserving storage and, thus, guarantee that the Intrusion Detection System (IDS) records cannot be altered and can be analyzed comprehensively. The system also has Quality of Service (QoS) rules and traffic shaping based on applications, which enables prioritization of critical services, such as VoIP, video conferencing, and business applications, as well as de-prioritization of non-essential traffic, such as downloads and updates. Mininet can simulate real-time SDN scenarios because it is used to prototype whole architectures. It is also compatible with controllers OpenDaylight and Ryu. It has tested the framework using the InSDN dataset and proved that it can identify different kinds of cyberattacks and handle bandwidth allocation efficiently under circumstances of resource constraints. SmartSecChain-SDN comprehensively addresses SDN system protection, securing and enhancing. The proposed study offers an innovative, extensible way to improve cybersecurity, regulatory compliance, and the administration of next-generation programmable networks.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 34 references that are in the map. First posted on arXiv 7 Nov 2025.
Builds on
9 of 34 references.
References · 9
- 2022HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detectioncited by 5
- 2023Blockchain and Deep Learning-Based IDS for Securing SDN-Enabled Industrial IoT Environmentscited by 2
- 2025Evaluating Machine Learning-Driven Intrusion Detection Systems in IoT: Performance and Energy Consumptioncited by 2
- 2025Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoTcited by 2
- 2021Decentralised Trustworthy Collaborative Intrusion Detection System for IoTcited by 1
- 2024S-HIDRA: A blockchain and SDN domain-based architecture to orchestrate fog computing environmentscited by 1
- 2024An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attackscited by 1
- 2024Strategic Deployment of Honeypots in Blockchain-based IoT Systemscited by 1
- 2024C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networkscited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.