Abstract Block chain technology is regarded for enhancing the characteristics of security because of decentralized design, safe distributed storage, and privacy. However, in recent times the present situation of block chain technology has experienced some crisis that may delay the quick acceptance and utilization in real-time applications. To conquer this subdues, a blockchain based system for attack detection and mitigation with Deep Learning (DL) named Fractional Tasmanian Devil Harris Optimization_Zeiler and Fergus network (FTDHO_ZFNet) is introduced. In this investigation, the entities utilized are owner, block chain, server, trusted authority and user. Here, authentication phase is done by means of Ethereum block chain by Key Exchange module and privacy preserved data sharing and communication is also done. Then, recorded log file creation is executed by the below mentioned stages. At first, a log file is generated with the basis of communication to record the events. After wards, the features are extracted by BoT-IoT database. Then, feature fusion is done by overlap coefficient utilizing Deep Q-Network (DQN). Moreover, data augmentation (DA) is doneusing bootstrapping method. At last, attack detection is observed by ZFNet tuned by FTDHO. Here, FTDHO is unified by Fractional Tasmanian Devil Optimization (FTDO) and Harris Hawks Optimization (HHO). Additionally, FTDO is integrated by Fractional Calculus (FC) concept and Tasmanian devil optimization (TDO). Furthermore, attack mitigation is performed. The performance measures applied for FTDHO_ZFNet are accuracy, and True Negative rate (TNR), observed supreme values with 92.9%, 93.8% and 92.9%.