Software-Defined Networking (SDN) has revolutionized the networking landscape by offering programmable control and optimization of network resources. However, SDN architecture’s inherent flexibility and centralized control expose it to new security risks. In this paper, we have presented a comprehensive study focused on the security implications associated with the control plane of SDN, which serves as a critical layer responsible for its network orchestration. We have addressed some pressing security concerns in SDN deployments by examining control plane vulnerabilities and explicit attacks. Through extensive analysis, we have investigated various control plane attacks. By meticulously exploring each attack vector, we have shed light on its mechanisms, potential impact and countermeasures. Furthermore, we have emphasized the interdependencies between the control plane, application plane, and data plane, highlighting how compromises in the control plane can propagate and impact the entire network infrastructure. Our research contributes to a deeper understanding of the specific vulnerabilities within SDN, focusing on the control plane as the primary target. By providing insights into the security landscape of SDN, network administrators, researchers, and security practitioners can develop proactive defense strategies and fortify the security posture of SDN deployments. We have underscored the importance of integrating robust security mechanisms to safeguard the control plane and maintain the overall security of SDN architectures. Our comprehensive analysis of control plane attacks in SDN elucidates the evolving security challenges posed by the programmability and centralization of network control. By addressing these vulnerabilities, we have tried to pave the way for future researchers to develop effective security solutions and ensure SDN networks’ resilience and integrity.

Data privacy is one of the intriguing problems in e-commerce site. For personal or business purposes, users have to disclose their private data to these e-commerce sites. Often such businesses use these highly sensitive data for computing artificial intelligence-driven analyses like recommendation generation without user consent. In the case of recommendation generation, data need to be analyzed at the business platforms. An automated personalization, based on artificial intelligence, on a list of products with respect to user interest is generated by a recommender system. However, the secure utilization of user data is absent in such systems. This paper proposes Private-Rec , a privacy-preserving platform for a recommendation system through the integration of artificial intelligence and blockchain. In Private-Rec , blockchain gives the user a secure environment through the distributed attribute in which data can be used with the required permission. Under this platform, users receive incentives (i.e., point, discount) from the recommended company for sharing their data to be used for computing recommendations. The Private-Rec platform has been studied empirically.

Cross-border data sharing for knowledge generation is a challenging research direction since an application may access personal data stored in countries different from the one where the application is accessed from. In this article, we propose a cross-border data sharing platform where a global cloud is built atop multiple security gateways that are set up in different countries. Once an application requests access to data from a particular country or region, the global cloud collects the data stored in local data hubs through that region's security gateway. While transferring the data to the global cloud, the security gateway records this transfer information on a blockchain maintained by the global cloud. When an application reports any misbehavior (e.g., providing wrong data type or incorrect data) against a security gateway, the global cloud verifies the claim by auditing the blockchain and punishes the misbehaving security gateway if the claim is true. In the case of false misbehavior report, the application itself will be punished by the global cloud. Thus, our platform provides an accountable data sharing function using blockchain that relies on a relaxed trust assumption on the data providers. We include five algorithms to handle data access request, data sharing, blockchain transaction, detecting, and punishing misbehaving entities. In the algorithms, we also introduce how the transaction takes place in the platform. Thus, the proposed platform is able to handle misbehaving data sender, data receiver, or any entity participating in the platform. We analyze our platform empirically by showing different graphs, which have been generated by a number of experiments on blockchain environment. We also delineate how the multilayer of signature (Elliptic Curve Digital Signature Algorithm) acts in our platform.