Network intrusion detection is crucial for identifying unauthorized activities in computer networks. Classification methods are key tools for this purpose, requiring high accuracy and low false alarm rates. A simplified network intrusion detection system includes data collection, preprocessing, and data mining modules. Preprocessed data are analyzed using clustering and classification techniques. Clustering identifies outliers and groups similar data, while classification assigns labels to incoming data based on labeled data. The results are analyzed and stored in a knowledge base, which must be updated regularly. Data mining methods, such as k-means, SOM, EM, and BIRCH, are used for clustering. Selection of methods depends on technical expertise, resources, and performance. The kernel-based MCMP model is applied to network intrusion detection using two datasets: NeWT and KDDCUP-99. NeWT, collected by the STEAL lab, includes simulated attacks and normal data. It has three classes: probe, other attack, and normal records. KDDCUP-99, provided by DARPA, is a standard dataset for intrusion detection experiments, containing TCP dump data from a simulated LAN. NeWT data includes 34,929 records, with 4,038 probes, 1,013 other attacks, and 29,878 normal records. Non-numeric attributes are transformed or removed, resulting in 23 attributes per record. KDDCUP-99 data includes nine weeks of raw TCP dump data with multiple attacks. The data mining process involves preprocessing, analysis, and classification to detect intrusions effectively.Network intrusion detection is crucial for identifying unauthorized activities in computer networks. Classification methods are key tools for this purpose, requiring high accuracy and low false alarm rates. A simplified network intrusion detection system includes data collection, preprocessing, and data mining modules. Preprocessed data are analyzed using clustering and classification techniques. Clustering identifies outliers and groups similar data, while classification assigns labels to incoming data based on labeled data. The results are analyzed and stored in a knowledge base, which must be updated regularly. Data mining methods, such as k-means, SOM, EM, and BIRCH, are used for clustering. Selection of methods depends on technical expertise, resources, and performance. The kernel-based MCMP model is applied to network intrusion detection using two datasets: NeWT and KDDCUP-99. NeWT, collected by the STEAL lab, includes simulated attacks and normal data. It has three classes: probe, other attack, and normal records. KDDCUP-99, provided by DARPA, is a standard dataset for intrusion detection experiments, containing TCP dump data from a simulated LAN. NeWT data includes 34,929 records, with 4,038 probes, 1,013 other attacks, and 29,878 normal records. Non-numeric attributes are transformed or removed, resulting in 23 attributes per record. KDDCUP-99 data includes nine weeks of raw TCP dump data with multiple attacks. The data mining process involves preprocessing, analysis, and classification to detect intrusions effectively.