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Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

Hilal Hacılar1, Bilge Kagan Dedeturk2, Burcu Bakir-Gungor1

  • 1Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey.

Peerj. Computer Science
|April 6, 2026
PubMed
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This study introduces a novel Deep Autoencoder (DAE)-based Artificial Bee Colony (ABC) algorithm for enhanced network intrusion detection. The new method significantly improves detection rates and reduces false alarms in cybersecurity.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cyberattacks are increasingly complex, challenging traditional intrusion detection systems.
  • Machine learning and deep learning are active research areas for network intrusion detection.
  • Conventional training algorithms for Artificial Neural Networks (ANNs) face optimization issues like local minima and slow convergence.

Purpose of the Study:

  • To develop an improved algorithm for training Artificial Neural Networks (ANNs) for network intrusion detection.
  • To address the limitations of conventional training methods and metaheuristics in ANNs.
  • To enhance the detection rate (DR) and reduce the false alarm rate (FAR) in network security.

Main Methods:

  • Implementation of a Deep Autoencoder (DAE) combined with a vectorized and parallelized Artificial Bee Colony (ABC) algorithm.
Keywords:
Anomaly detectionArtificial bee colonyArtificial neural networkDeep AutoencoderMetaheuristicsNF-UNSW-NB15-v2Network intrusion detection systems (NIDS)Swarm intelligenceUNSW-NB15

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  • Training feed-forward Artificial Neural Networks (ANNs) using the proposed DAE-based parallel ABC algorithm.
  • Testing the proposed method on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets for network intrusion detection.

    • Main Results:

    • The DAE-based parallel ABC-ANN demonstrated superior performance compared to existing metaheuristics.
    • On the UNSW-NB15 dataset, the proposed approach increased detection rate (DR) from 0.76 to 0.81 and reduced false alarm rate (FAR) from 0.016 to 0.005.
    • On the NF-UNSW-NB15-v2 dataset, the FAR was reduced from 0.006 to 0.0003 compared to the ANN-BP algorithm.

    Conclusions:

    • The proposed Deep Autoencoder (DAE)-based parallel Artificial Bee Colony (ABC) algorithm effectively enhances network intrusion detection.
    • The approach significantly improves detection accuracy and reduces false alarms, outperforming conventional methods.
    • This work offers a promising solution for strengthening network security against sophisticated cyber threats.