Predictive Modeling for Network Anomaly Detection Using Machine Learning

The protection of networks from unauthorized access is challenging, traditional cannot effectively prevent malicious intrusions. Now –a –days Anomaly detection relies heavily on machine learning to identify the complexity of Network Data Variability of Attack Patterns, Imbalanced Datasets. Automated anomaly detection can be accomplished without explicit programming by using supervised, unsupervised, or semi-supervised learning techniques to detect outliers in complex data sets. By harnessing the power of data-driven learning, machine learning algorithms exhibit the capacity to discern novel and previously uncharted attacks, dynamically adjust to shifting network conditions, and notably diminish instances of false positives and negatives. The incorporation of machine learning into intrusion detection proves indispensable as networks continue their evolution toward heightened complexity. This study presents four popular machine learning algorithms: Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest for anomaly detection in network. The CICIDS2017 dataset, known for its contemporaneity and extensive attack diversity, serves as the experimental foundation. Employing the Random Forest Regression algorithm, a meticulous process of feature selection was conducted. The ensuing application phase involved the utilization of distinct machine learning algorithms, resulting in remarkable performance outcomes. Specifically, the success rates achieved by various machine learning algorithms.