Traditional machine learning, mainly supervised learning, follows the assumptions of closed-world learning, i.e., for each testing class, a training class is available. However, such machine learning models fail to identify the classes, which were not available during training time. These classes can be referred to as unseen classes . Open-world Machine Learning (OWML) is a novel technique, which deals with unseen classes. Although OWML is around for a few years and many significant research works have been carried out in this domain, there is no comprehensive survey of the characteristics, applications, and impact of OWML on the major research areas. In this paper, we aimed to capture the different dimensions of OWML with respect to other traditional ML models. We have thoroughly analyzed the existing literature and provided a novel taxonomy of OWML considering its two major application domains - Computer Vision (CVIP) and Natural Language Processing (NLP). We listed the available software packages and open datasets in OWML for future researchers. Finally, the paper concludes with a set of research gaps, open challenges, and future directions.

Database is an important part for any organization and this database needs to make secure from various attacks in the network. Although there are various techniques implemented in the network for the security of these databases. Hare we proposed detection of intrusion is detected that are possible in the databases and various authentication techniques are implemented to made this databases secure. Database security and authentication such as 3 factor authentication, intrusion response system, timestamp, triggers such factors provides more security to the database. The proposed methodology implemented here provides security, authentication, and database policies.

Database is an important asset of any leading and emerging industry and this database needs to improved security features from various threats in the network and database repository. Most of an organization's sensitive and proprietary data resides in a Database Management System (DBMS). Data security is a major issue in any web-based application and database repository. Although there are various model implemented in the network for the security of these databases. Real world web databases have information that needs to be securely stored and accessed. In this paper we discussed proposed work classification based approach to create database policy dynamically and using these policy find intrusion in user request and response the request accordingly. The focus of our work is to develop advanced security solutions for protecting the data residing in a DBMS. Our approach is to develop an Intrusion Detection and Response (IDR) system. We introduce a policy-driven intrusion response mechanism that is capable of issuing an appropriate response based on the details of the anomalous request and discuss the implementation details on the same and report experimental results that show that our techniques are feasible and efficient.

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Information is the most valuable asset for organizations. Web database is combination of database and web technology. In Past few decades database user increasing very rapidly and the secrecy and the integrity are two important demands of security system. Data security is a major issue in any web-based application. Real world web databases have information that needs to be securely stored and accessed. Here we conclude various methods of anomaly detection, intrusion detection and access policy creation that are useful to solve security threats we also discuss various results and there merits and demerits.