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Research Proposal on Anomaly Detection by Applying the Machine Learning Technique

 Anomaly detection is the technique that helps to discover the unusual pattern in the data that fails to have the expected behavior referred to as outliers. The massive volume of data and the activities in the system induce challenges to detect the anomalies. The probabilities of anomaly detection are unbounded analytics systems struggled to explore the huge data to spot the anomalies of the system. There is a necessity for scalable systems that seek to automate the entire process. The anomalies rarely arise but reveal the vast and consequential threat in the system like cyber intrusions or fraud.
 The machine learning algorithm enables the anomaly detection system (ADS) with limited human intervention to accomplish the efficient anomaly detection system. The anomaly detection system is based on machine learning techniques that assist the companies in enhancing accuracy by automatically identifying the anomalies. The anomaly detection system discovers the extensive use of a diverse range of applications, including fraud detection in a credit card transaction, health care or insurance, fault identification in securing critical systems, and military surveillance.
 A typical machine learning-based anomaly detection discriminates the anomalies from normal behavior. However, determining the normal region of data among the feasible normal behavior is complex. Also, the normal behavior of the data is to evolve across the time that induces the issue during discrimination. Moreover, the imperfect data in the training data of the classification model makes anomaly detection much more difficult. Furthermore, the identification of anomalies along with the inadequate publicly available data set makes anomaly detection a more challenging task. The lack of description of the anomaly category intends to complexity while discriminating the new arrival of data. Storing and retaining the required massive data and constructing the computational capabilities for a long period is not economically possible.
 In anomaly detection, the performance of the detection system relies on the quality of training data since learning from the erroneous sample severely degrades the accuracy. Therefore, there is a necessity of constructing the well-labeled training data for the learning of a machine-learning algorithm to identify the anomalies. Despite this, the training data of the machine learning algorithm encompasses the noise that acts like an actual anomaly in many cases that discriminates anomalies as a complex task. Also, the overfitting and the curse of dimensionality are the other issues of the anomaly detection system while classifying the behavior of the data.