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Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation

Farhan Ullah1,2, Ali Turab1, Shamsher Ullah3

  • 1School of Software, Northwestern Polytechnical University, Xian 710072, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary

This study introduces an improved Intrusion Detection System (IDS) for Internet of Things (IoT) security, using multimodal big data and transfer learning to achieve 98.2% accuracy in detecting network attacks.

Keywords:
big datacybersecuritygame theoryintrusion detection systemnetwork traffictransfer learning

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning

Background:

  • Internet of Things (IoT) devices are vulnerable to Distributed Denial of Service (DDoS) and flood attacks.
  • Intrusion Detection Systems (IDS) are crucial for IoT security but face complexity due to numerous network features.
  • Effective attack detection requires analyzing various attack details like references, types, and host information.

Purpose of the Study:

  • To develop an improved Intrusion Detection System (IDS) for enhanced Internet of Things (IoT) security.
  • To leverage multimodal big data representation and transfer learning for robust attack detection.
  • To formally validate the proposed approach using a game theory-based process.

Main Methods:

  • Crawling Packet Capture (PCAP) files to gather attack data and bytes.
  • Utilizing Spark-based big data optimization algorithms for efficient data handling.
  • Employing word2vec for transfer learning to extract semantically-based features.
  • Converting network bytes into images and using an attention-based Residual Network (ResNet) for texture feature extraction.
  • Combining text and texture features for multimodal classification of attacks.

Main Results:

  • The proposed method achieved an excellent classification accuracy of 98.2% on three IoT datasets (CIC-IoT 2022, CIC-IoT 2023, Edge-IIoT).
  • Demonstrated effective handling of large volumes of data using big data optimization algorithms.
  • Successfully combined text and texture features for improved attack classification.

Conclusions:

  • The developed IDS effectively enhances IoT security against various network attacks.
  • The multimodal big data and transfer learning approach provides a robust solution for complex IoT security challenges.
  • The integration of game theory offers a formal validation of the system's effectiveness.