Cyber-Attack Detection in a Network

Abstract

Cyber-attack detection can identify unknown attacks from network traffics and has been an effective means of network security.  Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, RF, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model is proposed. The cyber-attack detection model uses Logistic Regression classifier and Multi-Layer Perceptron algorithms for better efficiency in prediction.