ISM-AC: an immune security model based on alert correlation and software-defined networking

Anomaly-based detection techniques have a high number of false positives, which degrades the detection performance. To address this issue, we propose a distributed intrusion detection system, named ISM-AC, based on anomaly detection using artificial immune system and attack graph correlation. To analyze network traffic, we use negative selection, clonal selection, and immune network algorithms to implement an agent-based detection system. ISM-AC leverages the programmability of software-defined networking to reduce the false positive rate. Our findings show that ISM-AC achieves better detection performance for denial of service, user to root, remote to local, and probe attack classes. Alert correlation plays a key role in this achievement.