This paper is deliberated to provide a model for Host-based Intrusion Detection and Prevention (HIDPS). HIDPS is increasingly becoming important to protect the host computer systems and its own network activities. HIDPS with intelligence is integrated into the computer systems to detect the intruder attacks activities, malicious Behaviour, application anomalies and protect the Information Systems from intruders and report the events to the HIDPS System Administrator. HIDPS is composed of software to monitor and analyze events occurring in the computer systems and information systems and to identify and stop potentially harmful incidents to the Systems. In this context, computer security is an essential property. HIDPS is one of the promising research areas of computer security as most of the security violations in systems occur due to malicious code and intruder activities being able to penetrate to the system barriers. Malicious code and intruder activities affect the computer systems by compromising integrity, confidentiality and availability of resources. It also changes the system Behaviour and extracts the system"s vital informations. This paper reviewed and compared the related various research papers on HIDPS to provide a suitable norm on HIDPS at two levels of intrusion detection and prevention i.e., user level and kernel level along with two phases of intrusion detection engines-Misuse and Anomaly detections for the best-fit system to any unique host computer systems. General TermsHost-based Intrusion Detection and Prevention System.

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The current decade has seen an increased usage of high-end digital technologies like machine learning in the field of health care services which enable in supporting and performing different functions with less or no human interventions. The application of machine learning tools in the orthopedic area is gaining more popularity as it can support in analyzing the issues in a more comprehensive manner, provide accurate data, support in forecasting the pattern. It enables offering critical information for taking quick decisions by the medical practitioners in order to enhance the health and dietary care service delivery. The ML tools can support in collecting patient centric data related to orthopedic surgery and also estimate the postoperative complications, level of treatment modalities to be provided, and guide the medical practitioners in taking effective clinical device decisions. The ML approach also supports in providing prediction methods of implementing the ortho surgical outcomes. Furthermore, it can also guide in making better treatment procedures, forecast the patterns, and stream the health care management services for better patient recovery. This study implements a quantitative research approach which will support in sourcing the data from the respondents who are currently working as medical practitioners, orthopedic experts, and radiologists who use ML-based models in making critical decisions related to orthopedic surgery. The researchers chose nearly 149 respondents, and the information was analysed using the IBM SPSS package for gaining critical interpretation. The major analyses cover descriptive analysis, regression analysis, and analysis of variances.