Anomaly Detection Algorithms in Cybersecurity

Abstract

In Anomaly Detection Algorithms in Cybersecurity, this chapter go over the fundamentals of the algorithms and techniques used to spot cyber threats. These methods include statistical, machine learning, deep learning, and clustering. In order to find statistical outliers, traditional anomaly detection employs Gaussian Mixture Models, which model the system's normal behavior. In order to improve their anomaly detection abilities, SVM and RF learn on tagged datasets. Using autoencoders for complicated and multidimensional data sets, deep learning has stabilized anomaly detection. K-Means and DBSCAN are two alternatives that can cluster data points and find outliers. This chapter takes a look at the algorithms and how they're utilized for identifying insider threats, fraud, malware, and network intrusions. In order to improve cybersecurity anomaly detection methods and safeguard against a wide variety of digital threats, this chapter takes a look at existing algorithms and methodology, analyzes their uses, problems, and potential future developments.