Small and medium-sized enterprises (SMEs) are increasingly targeted by cyberattacks yet face persistent problems in countering them such as limited security budgets, small Information Technology (IT) teams, and diverse network setups. SMEs rely more on basic security controls that lack advanced and continuous detection measures. Timely intrusion detection is crucial for SMEs, but most existing intrusion detection systems (IDS) are engineered for large organizations, as they require costly hardware, high maintenance, and expertise, conditions that SMEs cannot meet in most cases. Unlike existing hybrid systems, this study introduces a modular hybrid IDS designed for SME operational constraints. The system employs a dual detection layer, network-based and host-based, enhanced with external threat intelligence, machine learning and risk scoring. Its novelty lies in SME-specific custom detection rules, adaptive alerting mechanisms, and lightweight architecture. The system was validated in a real SME across 21 controlled attack scenarios (seven attack types, three repetitions each), with detection accuracy, false positive rate, response time, CPU utilization, and rule maintenance effort measured. The system achieved 95.2% detection accuracy, a 4.1% false positive rate, and an average response time of 2.3 seconds, while maintaining CPU and memory usage below 65% and 77%, respectively. Weekly rule updates require less than 25 minutes. By delivering enterprise-grade detection performance in a cost-efficient design, this work bridges the gap between IDS research and SME realities. The findings establish a foundation for future SME-focused security architectures, including automated rule tuning and expanded threat intelligence integration.