Intrusion Detection is a protection device that tracks and identifies inappropriate network behaviors. Several computer simulation methods for identifying network infiltrations have been suggested. The existing mechanisms are not adequate to cope with network protection threats that expand exponentially with Internet use. Unbalanced groups are one of the issues with datasets. This paper outlines the implementation and study on classification and identification of anomaly in different machine learning algorithms for network dependent intrusion. A number of balanced and unbalanced data sets are known as benchmarks for assessments by NSLKDD and CICIDS. For deciding the right range of options for app collection is the Random Forest Classifier. The chosen logistic regression, decision trees, random forest, naive bayes, nearest neighbors, K-means, isolation forest, locally-based outliers are a group of algorithms that have been monitored and unmonitored for their use. Results from implementations reveal that Random Forest beats the other approaches for supervised learning, though K-Means does better than others.

Blockchain is using in every aspect now because of its distributed ledger which is immutable. It provides the information to the users directly without any third party involvement. It mediates the transactions directly between the interacting parties securely. It also eliminates the friction and also the cost of current intermediaries. It is now using in healthcare system to provide the interoperability, security, decentralization and other. EMR is presently using in healthcare which has some issues. The issues in healthcare are patient cannot access the data of his/her own health information. So by this healthcare has issues like interoperability and delay in communication and some other. These issues can be solved by using the Blockchain in healthcare. By this Blockchain provide security by giving the patients to access their own data rather than provider.

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