Optimized Blockchain-Based Deep Learning Model for Cloud Intrusion Detection

Cyberattacks are becoming increasingly complex and subtle. In many different types of networks, intrusion detection systems, or IDSs, are frequently employed to help in the prompt detection of intrusions. Blockchain technology has gained a lot of attention recently as a means of sharing data without reliable third party. Specifically, it is impossible to change data stored in one block without changing all the following blocks. Create a deep learning (DL) method based on blockchain technology and hybrid optimization to improve the IDS's prediction accuracy. The UNSW-NB15 dataset is gathered via the Kaggle platform and utilized for Python system training. Principal component analysis (PCA) is used in the preprocessing to eliminate errors and duplication. Next, employ association rule learning (ARL) and information gain (IG) approaches to retrieve pertinent characteristics. The greatest features are the ones that improve detection performance through hybrid seahorse and bat optimization (HSHBA) selection. Lastly, create an efficient intrusion detection system by designing Blockchain-based Ensemble DL (BEDL) models, with convolutional neural networks (CNNs), restricted Boltzmann machines (RBM), and generative adversarial networks (GAN). The constructed model's experimental results are verified using pre-existing classifiers, yielding an improved accuracy of 99.12% and precision of 99%.