A Novel Secure Apriori Honey based Machine Learning Mechanism for Securing the Transactional Data

Frequent Itemset Mining (FIM) is utilized for identifying frequent itemsets that is simultaneous to the transactional database (TD). However, the rapid increase of the applications based on big data introduces the attacks in the database from unauthorized persons. So, a novel secure Apriori Honey based Machine Learning (SAHML) model is developed in this research to secure the transactional data from the third users. Here, the proposed SAHML model is to detect the malicious activities and protects the information in the database. This model is simulated using Java and provide high performance. Also, the experimental results are compared with existing models, which proves the competence of the SAHML approach.