Subject Review: Anomaly Detection in Cyber Security Using Convolution Neural Network

Abstract

Security concerns are multiplying as a result of the rapid advancement of computer and communications technology. Cybersecurity is evolving into different types of techniques to reduce these concerns. Amongst these techniques is anomaly detection (AD). Anomaly identification (AI) is a major problem in many academic fields and practical applications. It has been presented that Convolution Neural Networks (CNNs) be used to identify anomalies from different type; however, there is currently no guide over which model to apply in a specific instance. In this study presented the most relevant Convolution Neural Networks (CNNs) as a key solution for anomaly detection (AD) in the literature, compares between anomaly detection (AD) techniques which current in the previous studies. In addition, the pros and cons of this technique are examined in various application contexts, and their results are presented. Finally, this study offers a number of recommendations for future studies that will assist readers in their subsequent efforts in this field.