Optimizing Security Operations: Hybrid Intrusion Detection Systems Leveraging Data Engineering

Traditional intrusion detection systems (IDS) incapacity to keep up with the ever-evolving cyber threats is the reason for their poor threat identification skills and high false positive rates. A hybrid intrusion detection system (HIDS) employing existing data engineering methods is proposed in the study as a remedy for these issues. To provide an integrated method of threat prevention, the HIDS blends machine learning (ML) driven anomaly detection with signature-based identification. Using a sequence of procedures including data collecting, preprocessing, feature selection, model training, and real-time monitoring, the HIDS lowers false positive rates, boosts computation efficiency, and enhances detection accuracy. The proposed system outperforms the existing system according to results and analysis; it has higher accuracy (0.95), lower false positive and false negative rates (0.03), better detection rates across a range of attack types, and increased computational efficiency (30% CPU usage, 500 MB memory consumption, 50 ms detection speed). Overall, the HIDS provides companies with robust defenses against a range of cyber threats, demonstrating a proactive and adaptable attitude to cybersecurity.