Convolution Neural Network-Based Efficient Development of Intrusion Detection Using Various Deep Learning Approaches

Abstract

As internet usage has increased, firewalls and antiviruses are not alone enough to overcome the attacks and assure the privacy of information in a computer network, which needs to be a security system with multiple layers. Security layers are a must for protecting the network system from any potential threats through regular monitoring, which is provided with the help of IDS. The main objective of implementing intrusion detection is to monitor and identify the possible violation of the security policies of the computer system. Working preventively rather than finding a solution after the problem is essential. Threat prevention is done using intrusion detection systems development based on security policies concerning integrity, confidentiality, availability of resources, and system data that need to be preserved from attacks. In this research, three algorithms, namely Artificial Neural Network (ANN), Multi-Layer Perceptron (MLP), and Convolution Neural Network (CNN), have been used. From the results obtained, the proposed Convolution Neural Network (CNN)produces an Accuracy of 90.94%, MSE of 0.000242, Log Loss of 0.4079 and Mathews Coefficient of 0.9177. The tool used is Jupyter Notebook, and the language used is Python.