Anomaly Detection in Network Traffic using Machine Learning and Deep Learning Techniques

Abstract

Due to the rise of sophisticated cyberattacks, network security has become an increasingly important field. One of the most common threats to the security of networks is network anomalies, which can cause system malfunctions and prevent them from working properly. Detecting such anomalies is very important to ensure the continued operation of the network. Deep learning and machine learning algorithms have demonstrated their ability to detect network anomalies, but their effectiveness is still not widely known. This paper presents an evaluation of the performance of three algorithms against the KDD-NSL dataset. This study aims to provide a comprehensive analysis of the various techniques used in deep learning and machine learning to detect network anomalies. It will also help improve the security of networks. The paper presents an evaluation of the performance of three algorithms against the KDD-NSL dataset. The three algorithms are the Support Vector Machine, the Random Forest, and the Artificial Neural Network. They will be compared with their accuracy, recall, and F1-score. The study also explores the impact of the algorithm's feature selection on its performance. The findings of the investigation will be used to inform the development of new techniques that can be utilized to enhance the security of networks. The KDD NSL dataset provides an ideal opportunity to analyze the performance of various algorithms for detecting network anomalies.