Modelling of Blockchain Assisted Intrusion Detection on IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning

An IoT healthcare system refers to the use of Internet Things (IoT) devices and technologies in industry. It involves integration various interconnected devices, sensors, systems collect, monitor, transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on is an innovative approach enhancing security privacy sensitive data. By combining decentralized immutable nature blockchain technology with (IDS), it possible create a more robust trustworthy framework systems. With this motivation, study presents Blockchain Assisted Healthcare System using Ant Lion Optimizer Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables sector securely detects intrusions system. To accomplish this, performs ALO based feature subset selection (ALO-FSS) produce series vectors. HDL model integrates convolutional neural network (CNN) features long short-term memory (LSTM) detection. Lastly, flower pollination algorithm (FPA) exploited optimal hyperparameter tuning approach, which results enhanced rate. experimental outcome was tested two benchmark datasets outcomes indicate promising performance over other models.