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GANBOT: a GAN-based framework for social bot detection.

Shaghayegh Najari1, Mostafa Salehi1,2, Reza Farahbakhsh3

  • 1Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.

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Summary
This summary is machine-generated.

This study introduces GANBOT, a novel framework for detecting malicious social bots. GANBOT enhances bot detection by using generative adversarial networks (GANs) to improve contextual LSTM models, outperforming existing methods.

Keywords:
Deep neural networksGenerative adversarial networksSocial bot detectionText classification

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Social Network Analysis

Background:

  • Social media platforms are increasingly populated by malicious social bots designed to deceive users.
  • Existing bot detection methods, including statistical machine learning and Sequence Generative Adversarial Nets (SeqGAN), face limitations in accurately identifying sophisticated bots, especially those with evolving behaviors.
  • There is a critical need for advanced techniques to distinguish fake social accounts from genuine users.

Purpose of the Study:

  • To address the limitations of current bot detection methods, particularly the convergence issues in Sequence Generative Adversarial Nets (SeqGAN).
  • To propose and evaluate a novel framework, GANBOT, that leverages Generative Adversarial Networks (GANs) to enhance the performance of state-of-the-art textual bot detection models.
  • To improve the accuracy and robustness of social bot detection by extracting deeper behavioral patterns from bot samples.

Main Methods:

  • Utilized a semi-supervised Generative Adversarial Network (GAN) approach to extract behavioral patterns from social bot data.
  • Developed a new framework, GANBOT, where the generator and classifier are interconnected via an LSTM layer acting as a shared communication channel.
  • Integrated GAN-generated data to augment low-labeled datasets for training a Contextual LSTM model, a leading textual bot detection method.

Main Results:

  • The proposed GANBOT framework demonstrated superior performance compared to the existing Contextual LSTM method on a benchmark Twitter social bot dataset.
  • The integration of GAN-enhanced features led to a significant increase in bot detection probabilities.
  • The novel architecture effectively addressed the convergence limitations associated with original textual GANs (SeqGAN).

Conclusions:

  • GANBOT represents a significant advancement in social bot detection, offering improved accuracy and robustness.
  • The framework's ability to capture complex behavioral patterns makes it effective against evolving bot tactics.
  • This research highlights the potential of customized GAN architectures for enhancing deep learning-based detection systems in cybersecurity applications.