Published Paper Details:

Intrusion detection systems (IDS) are secure for protecting computer networks, and this study suggests an efficient IDS designed specifically for small to medium-sized networks. The system combines the decision tree algorithm with a machine learning framework by blending traditional anomaly detection with signature-based techniques. This combination provides a comprehensive system for detecting and responding to suspicious activities. The system strikes a compromise between accuracy and resource utilization by using lightweight algorithms and rule-based procedures, with a focus on the decision tree algorithm. The integration improves intrusion detection accuracy and overall system performance. The system's simplicity allows for quick deployment and maintenance, making it an effective solution for network security reinforcement. Testing findings confirm its ability to identify frequent network intrusions, making it a practical and cost-effective security solution for businesses with small to medium-sized networks.