Detection of Cyber Attacks in Network Traffic using Machine Learning Algorithm

Cyber-attack refers to any hostile attempt to obtain unauthorized access to a computing system or network with the intention of causing damage to the system. These attacks mainly aim at altering, disrupting and blocking the data sent by legitimate users. The main objective of this work is to study the behavior of attacks based on network traffic flow during normal and abnormal time interval. Four different Machine Learning (ML) models such as Linear Regression, Random Forest, Decision Tree and Naïve Bayes are employed and their parameters are analyzed, based on performance analysis, Decision tree has achieved a maximum prediction accuracy of 99% compared to other models.