Paper Title
Intrusion Detection in Network Using the Machine Learning Techniques

Abstract
In classification tasks, accuracy metrics may overlook precision and recall causes crucial for specific applications. We aim to create an evaluation framework that systematically assesses precision and accuracy across classifiers, facilitating optimal model selection based on tailored performance criteria and enhancing decision-making in diverse domains. Concerning cyber threats, network security mechanisms such as intrusion detection systems (IDS) detect unwanted activities. However, traditional signature-based IDS approaches can fail against new or emerging threats, therefore, machine learning (ML)-based techniques can be a good alternative. This paper describes the implementation of multiple machine-learning algorithms for IDS on a dataset with known class labels. The data is preprocessed, and the features are selected and balanced using SMOTE. The results show random forest works the best here, achieving 96% accuracy, while the others were not as successful. This study emphasizes the significance of incorporating feature selection and data balancing techniques to improve the performance of IDS and indicates the possibilities of future advancements in intrusion detection systems. This paper investigates the role of machine learning in anomaly detection for network security. Anomaly detection techniques aim to identify patterns in network traffic that deviate from expected behaviour, potentially indicating intrusions, zero-day attacks, or system failures. Machine learning algorithms, particularly unsupervised methods, offer a promising approach for anomaly detection as they can learn normal network behaviour without requiring pre-labelled attack data. However, the effectiveness of machine learning models heavily relies on selecting appropriate features from the network traffic data. Inappropriate feature selection can lead to misclassifications of anomalous traffic, especially in complex networks. Keywords - Feature Selection, Intrusion Detection System, Machine Learning, Network Security.