Comparative Analysis of Classification Techniques for Network Anomalies Management

In recent years, cyber-attacks have been a serious threat to governments, businesses, and individuals. Many Intrusion Detection Systems were designed to prevent these cyber-attacks, however, these systems are facing some difficulties to be efficient as the threats are growing every day. While advances in machine learning have led to numerous powerful classifiers, their usefulness for modeling anomalies detection system still needs to be explored.The main purpose of this study is to investigate the performance of some well-known machine learning algorithms with the aim of enhancing the Network Anomaly Detection System (NADS). This study compares the performance of four selected machine learning algorithms, i.e., K-Nearest Neighbors (KNN), K-Means, Naïve Bayes and Random Forest. This comparison is conducted to improve the overall performance and speed up the classification framework. In addition, the PCA Algorithm was used in reducing the number of used features by preserving the essential parts that have more variation of the features and remove the non-essential features with a fewer variation. Several experiments have been conducted using “KDD CUP99” dataset that is widely used to evaluate intrusion detection prototypes. The experimental outcomes demonstrate that KNN algorithm perform well in terms of accuracy and computation time.