Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.

Technology has revolutionized various aspects of human life, and networking plays an important role in this revolution. Networks have gained significant importance with the evolution of technology. Traditional networks have served human life for decades but cannot provide future networking requirements. Software-defined networking (SDN) is a major paradigm shift in the networking field; it provides an efficient way of managing and controlling networks by separating the control layer from the data layer. SDN has gained significant attention from researchers and industry experts in recent years. However, to accept SDN as a replacement for traditional networking, several areas need to be improved, and security is one of them. This exhaustive literature survey presents a detailed analysis of security solutions proposed by researchers for SDN security. This literature review can be classified into three main categories of SDN security solutions: conventional network security solutions, machine learning (ML)-based, and blockchain-based security solutions. A detailed domain analysis has been performed, and the proposed strategies and their results have been evaluated. Moreover, the shortcomings and contributions of previous approaches toward scientific society have been analyzed to facilitate the new research. This research has completed a detailed and diversified literature review to have wide concrete knowledge regarding the future trends for designing and developing innovative SDN security solutions. An exhaustive literature review revealed that various ML-based SDN security approaches have been proposed, including convolutional neural network (CNN), support vector machine (SVM), and k-nearest neighbors (KNN). Among these ML algorithms, CNN and SVM were found to be more effective and state-of-the-art in securing the SDN. Parametric analysis of different researchers demonstrated that blockchain-based flow rules, blockchain-based packet parsers, and controller authentication outperformed while securing the SDN networks. The paper covered all the aspects of previous research that have been experimented with to determine the robust and concrete techniques for the security of SDN networks.

Software Defined Networking (SDN) enhances network programmability and management by decoupling control and data planes. However, this logically centralized control plane introduces scalability issues and security vulnerabilities. In multi-domain SDN architectures malicious entities can compromise controllers to inject false flow rules, disrupting network integrity. Existing solutions often rely on static cryptographic authorizations, failing to monitor the real-time operational integrity of controllers. To address these limitations, this paper proposes an enhanced blockchain-based control layer security framework in multi-domain SDN (EBCS-SDN). The framework introduced a dynamic trust scoring model, an optimized dual-phase controller authentication mechanism and a decentralized behavioral-deviation hijack detection system to ensure continuous controller accountability. The empirical evaluations in a simulated multi-domain SDN environment demonstrated significant performance and security improvements over existing baselines models Voting-based, Proof-of-work (PoW)-based, DLCA_R_P, and BCS. Simulations in an emulated environment show that the proposed framework optimally reduces controller authentication latency by up to 75% and achieves a hijack detection accuracy of 96.78%. Under these simulated proof-of-concept conditions, the system demonstrated a post-attack throughput of 980 Mbps with detection and isolation times under 1.2 s. The proposed framework optimized the CPU utilization at full network nodes. Ultimately, the proposed system provides a scalable, computationally lightweight, and operationally resilient security foundation for SDN deployments.

Software-Defined Networking (SDN) has transformed network management by decoupling control and data planes, enabling centralized control and programmability. However, this centralization introduces a critical single point of failure (SPoF) in the control plane, rendering the network vulnerable to breaches. The multi-controller architecture enhances scalability and resilience but complicates security management. Although challenges such as consensus delays and scalability persist, decentralized and tamper-proof blockchain ledgers offer a promising security solution for SDN. This paper proposes Hierarchical Optimized Practical Byzantine Fault Tolerance (HOPBFT), a novel consensus framework that integrates Raft-based intra-domain consensus with an optimized hierarchical Practical Byzantine Fault Tolerance (PBFT) inter-domain layer, enhanced by blockchain technology for improved security and coordination. Evaluated in a simulated multi-domain SDN environment using Mininet and Ryu controllers, HOPBFT outperforms the baselines (e.g., DLCA_R_P, Voting Based, Proof of Work) with 400 transactions per second (TPS) at 50 controllers, a latency of 340 ms, and a success rate of 96% compared to DLCA_R_P (160 TPS, 1170 ms, 91%). The communication complexity of HOPBFT is \(\:O\left(n\text{l}\text{o}\text{g}\:n\right)\) compared to traditional PBFT \(\:O\left({n}^{2}\right)\) to ensure scalability and efficiency. The key contributions of this study are a scalable consensus framework, an efficient two-phase agreement mechanism, and validated fault tolerance in multi-domain SDN settings.