Blockchain-Integrated Discrete Hopfield NeuralNetwork and Edge Attention Networks withDuck Swarm Optimization for Cloud PrivacyEnhancement
Cloud computing has transformed data management for businesses and individuals alike by making systemsmore scalable and economically viable. However, the distributed architecture in cloud computing inherently makes itvulnerable to highly sophisticated cyber threats, such as DDoS attacks, ransomware, cryptojacking, and many othersthat could compromise data confidentiality, integrity, and availability. Traditional intrusion detection systems facelimitations such as high false-negative rates and low adaptability to emerging threats. This manuscript proposes ablockchain-integrated discrete Hopfield neural network and edge attention networks with duck swarm optimization(Hop-MEA-Duck) to enhance cloud privacy and develop a robust, privacy-preserving intrusion detection framework forcloud environments. The framework assumes a two-level privacy mechanism. The former provides privacy through theAdaptive Blockchain Sharding Protocol with Hybrid Consensus (Hyb-BCSP), which enhances data security andscalability. The second tier comprises preprocessing using the Adaptive Self-Guided Loop Filter (ASGLF) and featureselection via the Pufferfish Optimization Algorithm (POA). The Discrete Hopfield Neural Network (DHNN) withMultilayer Edge Attention Network (MEAN) is used to classify normal and abnormal behaviors, and the Duck SwarmAlgorithm (DSA) is used at the expense of hyperparameter tuning. The results of the experiments indicate that theproposed framework is practical, achieving 97.8% detection accuracy, 96.5% precision, and a significant reduction inthe false-negative rate to 2.4%. Also, the preprocessing phase increased the relevance of the data by 85%, whereas therate of information immutability in the blockchain was 99.2%. To sum up, the suggested framework can provide aprivacy-preserving, scalable, and adaptable solution for detecting and removing cyber threats in the context of cloudcomputing. It can fill significant gaps in current intrusion detection approaches.