Network intrusion detection is crucial for maintaining network security, focusing on identifying inappropriate or anomalous activities. Classification methods are key tools in this process, aiming for high accuracy and low false alarm rates. A simplified network intrusion detection system involves data collection by sensors, preprocessing, and analysis using data mining modules such as clustering and classification. Clustering helps identify outliers and group similar data, while classification assigns labels to incoming data and triggers alerts. The outputs are analyzed by human analysts and stored in a knowledge base, which is periodically updated. The chapter discusses the application of the kernel-based MCMP model to network intrusion detection, tested using two datasets: NeWT and KDDCUP-99. The NeWT dataset, collected by the STEAL lab at the University of Nebraska, simulates various network attacks and normal activities. It includes four types of attacks: denial-of-service (DOS), remote-to-local (R2L), local-to-root (U2R), and probe. These are grouped into one class called "other attack." The dataset has 34,929 records, with 23 attributes after preprocessing. The KDDCUP-99 dataset, provided by DARPA, is a standard for intrusion detection experiments. It contains raw TCP dump data from a LAN simulating a U.S. Air Force network, with multiple attacks added to the traffic. The dataset includes about four gigabytes of compressed binary data from seven weeks of network traffic.Network intrusion detection is crucial for maintaining network security, focusing on identifying inappropriate or anomalous activities. Classification methods are key tools in this process, aiming for high accuracy and low false alarm rates. A simplified network intrusion detection system involves data collection by sensors, preprocessing, and analysis using data mining modules such as clustering and classification. Clustering helps identify outliers and group similar data, while classification assigns labels to incoming data and triggers alerts. The outputs are analyzed by human analysts and stored in a knowledge base, which is periodically updated. The chapter discusses the application of the kernel-based MCMP model to network intrusion detection, tested using two datasets: NeWT and KDDCUP-99. The NeWT dataset, collected by the STEAL lab at the University of Nebraska, simulates various network attacks and normal activities. It includes four types of attacks: denial-of-service (DOS), remote-to-local (R2L), local-to-root (U2R), and probe. These are grouped into one class called "other attack." The dataset has 34,929 records, with 23 attributes after preprocessing. The KDDCUP-99 dataset, provided by DARPA, is a standard for intrusion detection experiments. It contains raw TCP dump data from a LAN simulating a U.S. Air Force network, with multiple attacks added to the traffic. The dataset includes about four gigabytes of compressed binary data from seven weeks of network traffic.