A Digital DNA Sequencing Engine for Ransomware Detection Using Machine Learning

The emerging development of machine learning models and artificial intelligence frameworks in recent days enrich the optimization of computing process involved in system developments. The DNA computing is the process of effective computing of relativity components, dynamic responses extracted from various existing computing systems. It acts as a metaphor for exploring solutions through genetic behavior of data involved in the computing process. Conventional method of steganography is limited to versatile operation. In the proposed approach smith waterman algorithm is utilized to formulate the secure storage and processing of cloud data. Due to various external software involved in computing operations, the malicious activity in the cloud is increased. The proposed approach considers the hierarchal based attribute model for encryption. The proposed model considers smith waterman algorithm (SWA) for efficient optimization of DNA data. Apar from various challenges, the proposed system is developed with effective user accessible cloud platform for lightweight health care systems.