Predictive Analysis on Intrusion Detection System Using CNN: Machine Learning Algorithm

Abstract

In this research article the researcher emphasized on rapid expansion of the digital landscape, the security of networked systems has become a paramount concern. Network intrusions, which involve unauthorized access and malicious activities, pose significant threats to the confidentiality, integrity, and availability of sensitive information. To counter these threats, intrusion detection systems (IDS) play a crucial role in identifying and mitigating such intrusions. In recent times, machine learning algorithms have gained prominence in enhancing the accuracy and efficiency of IDS.This study presents a comprehensive investigation into network intrusion and intrusion detection techniques, focusing on the utilization of the CNN algorithm from the classification of machine learning approach to identify the group of intrusion and non intrusion data. The main objective of implementation of CNN algorithm adapted to the context of intrusion detection due to its ability to discover patterns in large datasets. The researcher found the 99% accuracy level using CNN Basic Performance Model and At 100/100, it takes 63s 631ms/step to lose 0.2421ms per step, and it finds a way 0.850ms per way to acquire 1.05ms.99.9% of the time has elapsed since Epoch started.