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Hybrid AI Intrusion Detection: Balancing Accuracy and Efficiency.

Vandit R Joshi1, Kwame Assa-Agyei1, Tawfik Al-Hadhrami1

  • 1Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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This study compares AI models for Internet of Things (IoT) intrusion detection. CNN-BiLSTM offers high accuracy, while XGBoost and Random Forest provide faster, competitive detection for diverse IoT needs.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • The Internet of Things (IoT) presents significant security challenges due to resource constraints, diverse protocols, and infrastructure heterogeneity.
  • Traditional Intrusion Detection Systems (IDS) struggle with IoT's scale, interoperability, real-time demands, data privacy, and imbalanced traffic leading to false positives.

Purpose of the Study:

  • To systematically evaluate and compare the performance and latency of representative AI models for IoT intrusion detection.
  • To provide empirical insights for selecting appropriate AI models based on accuracy-latency trade-offs in heterogeneous IoT environments.

Main Methods:

  • Comparative analysis of three AI models: Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM), Random Forest, and XGBoost.
Keywords:
Bi-LSTMIoT securityNSL-KDDUNSW-NB15convolutional neural network (CNN)hybrid modelsintrusion detection systemsperformance metrics

Related Experiment Videos

  • Evaluation conducted on two benchmark datasets: NSL-KDD and UNSW-NB15.
  • Quantification of detection performance (e.g., F1 score) and inference latency for each model.

    • Main Results:

    • CNN-BiLSTM achieved the highest detection capability with an F1 score up to 0.986, but incurred higher computational overhead.
    • XGBoost and Random Forest demonstrated competitive accuracy with significantly lower inference latency (sub-millisecond on conventional hardware).
    • A clear trade-off between detection accuracy and inference latency was observed across the evaluated models.

    Conclusions:

    • The choice of AI model for IoT intrusion detection depends on the specific application's requirements for accuracy versus speed.
    • CNN-BiLSTM is suitable for accuracy-critical applications, while XGBoost and Random Forest are viable for latency-sensitive scenarios.
    • These findings support informed deployment decisions for effective security in diverse IoT ecosystems.