Anomaly Detection using Clustering (K-Means with DBSCAN) and SMO

Abstract

In recent times, AI has become a useful tool for describing the properties of information because it can support the Data Mining (DM) procedure by analysing data for identifying patterns or routines. Anomaly detection is of vital importance in DM that helps in the discovery of hidden behaviour within the most vulnerable data. It also aids in the detection of network intrusion. This research proposed a model for detecting anomalies using machine learning (ML) techniques. By leveraging ML, the model can achieve higher detection rates and reduce the number of false positives, resulting in an overall improvement in intrusion classification. This study evaluated a proposed hybrid ML technology using dataset of Network Security Knowledge and Data Discovery. In this study, we used K-means and Density-Based Clustering Algorithm for clustering and Sequential Minimal Optimization for classification purposes. By putting the suggested method for detecting anomalies to test, it is demonstrated by the findings that this hybrid model can increase positive detection rate and anomaly detection accuracy while decreasing rate of false-positives. The proposed algorithm showed superior performance compared with recent closely related studies using similar variables and environments. This algorithm achieved lower false alarm probability (FAP) and high accuracy. This is due to the hybrid nature of producing an optimal detectors quantity that exhibit high accuracy and low FAP. The required time will decrease if the given false alarm probability is small for pre-processing and processing when compared to other algorithms.