Enhancement of Blockchain System in Online Transaction by Detecting Attacks Using an Intelligent Approach Recurrent Neural with Serpent Encryption (RNwSE)

Abstract

To maintain the security level in any application, implementing the malicious behavior detection approach is crucial. So, the present research work has intended the central concept of malicious behavior detection in the communication medium. In cloud applications, malicious event detection is a complex task because of the extensive unstructured data. The blockchain-based deep network has been introduced to predict malicious behavior to end these issues. Moreover, the detection-based blockchain model is Recurrent Neural with Serpent Encryption (RNwSE). The unknown or malicious characteristics were detected in the initial phase after the homomorphic serpent encryption model functioned. Moreover, we have implemented the planned work in the python frameworks. The scalability of the developed model has been found in terms of encryption-decryption duration and the exactness score of attack detection. Subsequently, the presented paradigm is compared with recently associated schemes and has earned the most satisfactory outcome as high exactness rate and less processing duration.