Project Overview
Computer network technologies are always evolving at a breakneck speed. A developing technology, the Internet of Things (IoT) has enormous potential for use across a wide range of sectors and social requirements. IoT is, however, constrained by a number of issues, including availability, security, scalability, energy, and resource restrictions. The Internet of Things (IoT) ecosystem is one of the many application domains where Software Defined Networking (SDN) and Blockchain (BC) have emerged as complementary technologies that support increased network performance and security, which should improve our collective quality of life. Software-Defined Networking (SDN) provides numerous characteristics that can address many of the shortcomings of the traditional Internet of Things system. By appropriately integrating SDN technology into the traditional IoT system, an enhanced IoT network system known as an SD-IoT system has been developed. This technology has the ability to address a number of IoT restrictions. Better energy and resources have enabled this more recent SD-IoT version to handle greater computational demands in order to address security concerns.
The major cyber threat in SD-IoT networks is DDoS attacks, which exploit the centralized SDN controller and resource-constrained IoT devices by overwhelming them with massive malicious traffic. Attackers often use compromised IoT devices to form botnets, flooding the network and exhausting resources. This leads to communication breakdowns, service outages, and severe performance degradation, making DDoS attacks a critical challenge to the security and reliability of SD-IoT systems.
Recently, Blockchain technology has emerged as a cutting-edge security-based technology that has already been successfully used in the cryptocurrency space. Adopting Blockchain-based technology in the SDN-based IoT networking space offers many research opportunities, however there are security challenges to be solved.
SD-IoT networks can become significantly more secured by integrating Machine Learning (ML) and Blockchain. Blockchain's decentralized and tamper-proof architecture ensures secure, trustworthy data for ML analysis, eliminating single points of failure and enhancing device authentication. ML adds intelligent, real-time anomaly detection, identifying and mitigating threats like DDoS attacks. Smart contracts automate threat responses, and Blockchain’s immutable logs provide transparency and accountability. Together, these technologies create a resilient, adaptive, and secure framework, addressing vulnerabilities and ensuring robust protection for SD-IoT networks
Therefore, the objectives of this proposal are as follows: we propose a BC_SD-IoT framework for the detection of cyber threats, specifically Distributed Denial of Service (DDoS) attacks. The framework leverages distributed model training using Federated Learning (FL) and supervised machine learning enhanced with Explainable AI (XAI) to identify malicious traffic packets. The control architecture is decentralized, where local controllers manage data flow and detection at the edge, while a central controller coordinates decision-making and response strategies across the distributed network. In addition, a Blockchain layer is integrated to maintain an immutable audit log of detected attacks, enabling the central controller to take appropriate actions whenever an attack is identified.
The major cyber threat in SD-IoT networks is DDoS attacks, which exploit the centralized SDN controller and resource-constrained IoT devices by overwhelming them with massive malicious traffic. Attackers often use compromised IoT devices to form botnets, flooding the network and exhausting resources. This leads to communication breakdowns, service outages, and severe performance degradation, making DDoS attacks a critical challenge to the security and reliability of SD-IoT systems.
Recently, Blockchain technology has emerged as a cutting-edge security-based technology that has already been successfully used in the cryptocurrency space. Adopting Blockchain-based technology in the SDN-based IoT networking space offers many research opportunities, however there are security challenges to be solved.
SD-IoT networks can become significantly more secured by integrating Machine Learning (ML) and Blockchain. Blockchain's decentralized and tamper-proof architecture ensures secure, trustworthy data for ML analysis, eliminating single points of failure and enhancing device authentication. ML adds intelligent, real-time anomaly detection, identifying and mitigating threats like DDoS attacks. Smart contracts automate threat responses, and Blockchain’s immutable logs provide transparency and accountability. Together, these technologies create a resilient, adaptive, and secure framework, addressing vulnerabilities and ensuring robust protection for SD-IoT networks
Therefore, the objectives of this proposal are as follows: we propose a BC_SD-IoT framework for the detection of cyber threats, specifically Distributed Denial of Service (DDoS) attacks. The framework leverages distributed model training using Federated Learning (FL) and supervised machine learning enhanced with Explainable AI (XAI) to identify malicious traffic packets. The control architecture is decentralized, where local controllers manage data flow and detection at the edge, while a central controller coordinates decision-making and response strategies across the distributed network. In addition, a Blockchain layer is integrated to maintain an immutable audit log of detected attacks, enabling the central controller to take appropriate actions whenever an attack is identified.