Network security is a critical concern in today's digital age, where cyber threats are becoming increasingly sophisticated and pervasive. Intrusion detection systems (IDS) are essential tools for detecting and mitigating such threats, and machine learning algorithms have shown great promise in this domain. In this report, we present our analysis of a network traffic dataset consisting of 43 features related to network activity, including duration, protocol type,service and source and destination bytes. We will be discussing all the features or columns in details in the next section. The dataset includes instances of various types of attacks, and the goal of our analysis is to build a machine learning model that can accurately classify these attacks. We first describe the preprocessing and feature engineering steps we performed on the dataset to prepare it for modeling. We then present the results of our experiments with different machine learning algorithms and parameter settings, and provide insights into the most effective approaches for intrusion detection using this dataset. Overall, our study aims to contribute to the development of more robust and effective IDS systems for network security.
In conclusion, we analyzed a network traffic dataset consisting of various features related to network activity and performed preprocessing steps to prepare the data for machine learning models. Our goal was to develop a machine learning model that can accurately classify various types of attacks based on network traffic data. We experimented with different machine learning algorithms, including Decision Tree, KNN, ANN, and KMEAN, and evaluated their performance using various metrics such as accuracy, precision, recall, and F1-score. We found that KNN achieved the highest accuracy of 99.70%, followed by Decision Tree with an accuracy of 99.80% and ANN with 99.82%. Our study provides insights into the most effective approaches for intrusion detection using network traffic data and machine learning algorithms. The results of our experiments demonstrate the potential of machine learning algorithms in detecting and mitigating cyber threats. However, there is still a need for more research in this area to further improve the accuracy and effectiveness of intrusion detection systems and We believe by understanding this domain of the data more will also help us increase it’s accuracy and performance more.