Optimizing Intrusion Detection Efficiency through Filtered clusterer, K-Mean clusterisn and Density-Based Clustering Without Count Attribute

Abstract

Anomaly detection is important in systems like intrusion detection, fraud detection and system monitoring. These systems have a problem with false positives. This can cause fatigue make the system less efficient and reduce confidence in it. This abstract looks at ways to deal with positives in anomaly detection. We will talk about threshold tuning, ensemble methods and semi-supervised learning. These techniques can help lower the positive rate. They can also make the system more accurate and robust. By using these techniques we can reduce fatigue. The system becomes more efficient. People trust it more. Anomaly detection systems become valuable, for applications. Anomaly detection helps in intrusion detection, fraud detection and system monitoring. Anomaly detection is a part of these systems. It helps to identify patterns. Anomaly detection is used in areas. It has some attributes which is responsible for system design. In this paper we have remove one attribute and provide explanation. By removing the attribute the result is compared.