Intrusion Detection System (IDS) is meant to be a software application which monitors the network or system activities and finds out any malicious operation occurs. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. Nowadays, hackers use different types of attacks for getting the valuable information. As the internet emerging into the society, new stuffs like viruses and worms are imported. The malignant, the users use different techniques like cracking of password, detecting unencrypted text are used to cause vulnerabilities to the system. Hence, security is needed for the users to secure their system from the intruders. Firewall technique is one of the popular protection techniques and it is used to protect the private network from the public network. IDS is used in network related activities, medical applications, credit card frauds, Insurance agency. Many intrusion detection techniques, methods and algorithms help to detect those attacks. The main objective of this project is to provide a comparative study about intrusion detection using various machine learning and deep learning techniques. Various machine learning techniques have been used to develop IDs, such as Random Forest algorithm, Support Vector Machine and Gradient boosting algorithm in real time network datasets such as Intrusion Detection System (IDS) datasets and UNSW datasets. Gradient boosting is a popular machine learning algorithm that can be used for intrusion detection in computer networks. The algorithm involves iteratively adding weak learners to a model to improve its overall predictive power. The proposed system can be analyzed in terms of error rate and accuracy values and implement in python tool for performance analysis.