Network intrusion detection remains a challenging research area as it involves learning from large-scale imbalanced multiclass datasets. While machine learning algorithms have been widely used for network intrusion detection, most standard techniques cannot achieve consistent good performance across multiple classes. In this dissertation, a novel ensemble system was proposed based on the Modified Adaptive Boosting with Area under the curve (M-AdaBoost-A) algorithm to detect network intrusions more effectively. Multiple M-AdaBoost-A-based classifiers were combined into an ensemble by employing various strategies, including particle swarm optimization. To the best of our knowledge, this study is the first to utilize the M-AdaBoost-A algorithm for addressing class imbalance in network intrusion detection. Compared with existing standard techniques, the proposed ensemble system achieved superior performance across multiple classes in both 802.11 wireless intrusion detection and traditional enterprise intrusion detection.