Securing the Digital Perimeter: A Comprehensive Intrusion Detection System with Ensemble Learning

The article introduces an all-inclusive Intrusion Detection System (IDS) for anomaly and misuse detection to handle rising computer network cybersecurity risks. Our intrusion detection system (IDS) design includes anomaly detection using the Self-Organizing Map (SOM) and abuse detection utilizing the Gradient Boosting Algorithm and AdaBoosting Algorithm as ensemble classifiers. For this, we use the large and diverse CICIDS dataset. SOM-based anomaly detection is adaptable to CICIDS dataset patterns. The SOM detects slight anomalies that may indicate intrusions by unsupervised learning of the dynamic nature of network functioning. The modern network is flexible despite complicated and dynamic traffic. The abuse detection module detects dataset attacks well using the Gradient Boosting Algorithm and AdaBoosting Algorithm. Ensemble techniques use multiple weak classifiers to enhance detection accuracy. The system's success against old and new cyber-attacks shows its adaptability. Our intelligent Intrusion Detection System (IDS) study found amazing findings for network anomaly and abuse detection. We leverage the huge dataset in our IDS architecture. Self-organizing map anomaly detection Gradient Boosting Algorithm and AdaBoosting Algorithm abuse detection are powerful. The CICIDS dataset is appropriate for testing the system's cyber risk identification and categorization due to its diversity and real-world application. After testing with the dataset and cutting-edge ensemble learning algorithms, the recommended intrusion detection system design appears to solve practical intrusion detection problems. These ensemble algorithms provide a strong defense, demonstrating the system's ability to warn cybersecurity analysts swiftly and accurately. Research could improve system scalability for big, varied networks and investigate fresh feature engineering methodologies.