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For data privacy, system reliability, and security, Blockchain technologieshave become more popular in recent years. Despite its usefulness, theblockchain is vulnerable to cyber assaults; for example, in January 2019 a 51%attack on Ethereum Classic successfully exposed flaws in the platform'ssecurity. From a statistical point of view, attacks represent a highly unusualoccurrence that deviates significantly from the norm. Blockchain attackdetection may benefit from Deep Learning, a field of study whose aim is todiscover insights, patterns, and anomalies within massive data repositories. Inthis work, we define an trusted two way intrusion detection system based on aHierarchical weighed fuzzy algorithm and self-organized stacked network (SOSN)deep learning model, that is trained exploiting aggregate information extractedby monitoring blockchain activities. Here initially the smart contract handlesthe node authentication. The purpose of authenticating the node is to ensurethat only specific nodes can submit and retrieve the information. We implementHierarchical weighed fuzzy algorithm to evaluate the trust ability of thetransaction nodes. Then the transaction verification step ensures that allmalicious transactions or activities on the submitted transaction byself-organized stacked network deep learning model. The whole experimentationwas carried out under matlab environment. Extensive experimental resultsconfirm that our suggested detection method has better performance overimportant indicators such as Precision, Recall, F-Score, overhead.

Detection Of Insider Attacks In Block Chain Network Using The Trusted Two Way Intrusion Detection System