Blockchain Mechanism-Based Attack Detection in IoT with Hybrid Classification and Proposed Feature Selection

Abstract

With IoT development, safety attacks are growing gradually as well. Numerous centralized attack-detecting schemes were developed for detecting IoT attacks namely, ML and DL schemes, which classify it as normal or attack. However, because to the various requirements of IoT devices, the most of the plans fall short of achieving noticeable results. In this study, a blockchain method is used to construct a novel attack-detecting scheme for IOT. Here, a system using blockchain is employed to process information in a logical sequence. At first, enhanced data normalization is deployed. Then, the features like “Exponential Moving Average, improved Holoentropy, higher-order statistical features, and mutual information” are extracted. Then feature selection is done via Improved Principal Component Analysis. The selected traits are evaluated “hybrid classification that includes Gated Recurrent Unit and Deep Belief Network” to discover the attacks in IoT. Finally, the advancements of this work are established over others. The proposed HC(GRU + DBN) scheme has attained higher accuracy of 97.2% for the best case of 92%,91%,94%,84%, and 92% to current schemes like SVM, RF, RNN, LSTM, and NB.