Networks have an important role in our modern life. In the network, Cyber security plays a crucial role in Internet security. An Intrusion Detection System (IDS) acts as a cyber security system which monitors and detects any security threats for software and hardware running on the network. There we have many existing IDS but still we face challenges in improving accuracy in detecting security vulnerabilities, not enough methods to reduce the level of alertness and detecting intrusion attacks. Many researchers have tried to solve the above problems by focusing on developing IDSs by machine learning methods. Machine learning methods can detect datas from past experience and differentiate normal and abnormal data. In our work, the Convolutional Neural Network(CNN) deep learning method was developed in solving the problem of identifying intrusion in a network. Using the UNSW NB15 public dataset we trained the CNN algorithm. The Dataset contains binary types of ‘0’ and ‘1’ in general for normal and attack datas. The experimental results showed that the proposed model achieves maximum accuracy in detection and we also performed evaluation metrics to analyze the performance of the CNN algorithm.