Outlier detection has been a very important concept in the realm of data analysis and the complex relationships that appear with regard to patient symptoms, diagnoses and behavior are the most promising areas of outlier. The data typically consists of records which may have several different types of features such as patient age, blood group and weight. Recently, density-based outlier detection has emerged as a viable and scalable alternative to traditional statistical and geometric approaches. Density Based Outlier Detection Algorithm along with K-means partition technique is used for detecting outliers in Heart Disease dataset which is used to diagnose the abnormal data. This analysis can be used by doctor to predict heart disease of particular patient.

A general class of temporal probabilistic model have recently developed, which extends the Forward, Backward and Viterbi algorithm for hidden Markov models. The HMM (Midden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The algorithm is based on shrinking the state space of the HMM noticeably using such chains. The states through which the world passes are hidden, or unobserved. However, at each point in time also gets an observation that in some way reflects on the current state of the world. The Cloud Computing is a big deal for three reasons: It does not need any effort on Clients part to maintain or manage. It's effectively infinite in size, so clients don't need to worry about it running out of capacity. User can access cloud-based applications and services from anywhere all you need is a device with an Internet connection. In this Cloud Computing used the Distributed File Systems (DFS) for sharing and allocating the data during dynamic process .Those process are using some Prediction algorithms here using HMM Forward and Backward Chain. In this paper represents, Cloud Storage Server can Share the data among with the multiple users, using two prediction algorithms such as forward and backward chain in HMM.

Distributed Denial of Service (DDoS) assault is a ceaseless basic risk to the web. Application layer DDoS Attack is gotten from the lower layers. Application layer based DDoS assaults utilize honest to goodness HTTP asks for after foundation of TCP three-way handshaking and overpowers the casualty assets, for example, attachments, CPU, memory, circle, database transfer speed. Arrange layer based DDoS assaults sends the SYN, UDP and ICMP solicitations to the server and debilitates the transfer speed. An oddity discovery system is proposed in this paper to identify DDoS assaults utilizing Enhanced Support Vector Machine (ESVM). The Application layer DDoS Attack, for example, HTTP Flooding, DNS Spoofing and Network layer DDoS Attack, for example, Port Scanning, TCP Flooding, UDP Flooding, ICMP Flooding, Land Flooding. Session Flooding is taken as test tests for ESVM. The Normal client gets to conduct characteristics is taken as preparing tests for ESVM. The movement from the testing tests and preparing tests are Cross Validated and the better arrangement exactness is acquired. Application and Network layer DDoS assaults are arranged with order exactness of 99 % with ESVM.