An Artificial Intelligence-based Intrusion Detection System

Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible to define every attack in the database and make the process costly also. Recently, research in conjunction with artificial intelligence and network security has evolved. As a result, it created many possibilities to enable machine learning approaches to detect the new attacks in network traffic. Machine learning has already shown successful results in the domain of recommendation systems, speech recognition, and medical systems. So, in this paper, we utilize machine learning approaches to detect attacks and classify them. This paper uses the CSE-CIC-IDS dataset, which contains normal and malicious attacks samples. Multiple steps are performed to train the network traffic classifier. Finally, the model is deployed for testing on sample data.