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Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm.

Bambang Susilo1, Abdul Muis1, Riri Fitri Sari1

  • 1Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
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This study enhances Internet of Things (IoT) security by using deep learning for attack detection. A multistage approach with autoencoders, LSTMs, and CNNs improves intrusion detection system performance against threats like DoS and Mirai attacks.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • The Internet of Things (IoT) is integral to daily life but faces significant security vulnerabilities due to architectural and technological challenges.
  • Effective security measures are crucial for comprehensive IoT protection.
  • Existing intrusion detection systems (IDS) often lack the sophistication to handle complex IoT threats.

Purpose of the Study:

  • To improve the performance of deep learning models for detecting attacks in IoT environments.
  • To address data imbalances and enhance learning outcomes using the synthetic minority over-sampling technique (SMOTE).
  • To develop a robust, multistage deep learning framework for enhanced IoT security.

Main Methods:

  • A multistage feature extraction process utilizing autoencoders (AEs) for initial feature extraction.
Keywords:
Internet of Things (IoT)autoencoderconvolutional neural network (CNN)deep learningintrusion detection systemlong short-term memory (LSTM)

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  • Long short-term memory (LSTM) networks to analyze temporal patterns and detect abnormal behavior.
  • Convolutional neural networks (CNNs) for final classification of security threats.
  • Application of SMOTE to mitigate data imbalances and improve model training.

    • Main Results:

    • The proposed framework effectively extracts robust features and identifies temporal patterns indicative of cyber threats.
    • The integrated AE-LSTM-CNN architecture demonstrates enhanced capabilities in classifying IoT security data.
    • The approach is specifically designed to detect critical attacks such as Denial of Service (DoS) and Mirai.
    • The multistage method offers a more comprehensive analysis compared to conventional single-model IDSs.

    Conclusions:

    • Deep learning methodologies, particularly the proposed multistage framework, significantly enhance the effectiveness of IDSs in IoT security.
    • The research highlights the potential for improved security measures and mitigation of emerging IoT threats.
    • The study validates the benefits of combining AEs, LSTMs, and CNNs with SMOTE for robust IoT cybersecurity.