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Explainable artificial intelligence for botnet detection in internet of things.

Mohamed Saied1, Shawkat Guirguis2

  • 1Institute of Graduate Studies & Research, Alexandria University, 832, Elhorrya Road, Alexandria, 21526, Egypt. igsr.msaied@alexu.edu.eg.

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Explainable AI (XAI) enhances Internet of Things (IoT) botnet detection by improving model transparency and trustworthiness. This research demonstrates XAI

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Area of Science:

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

Background:

  • The proliferation of IoT devices has increased connectivity but also introduced significant security challenges, particularly botnet attacks.
  • Detecting botnets in IoT environments is difficult due to device diversity and large data volumes.
  • AI and machine learning show promise for IoT botnet detection but lack transparency in decision-making.

Purpose of the Study:

  • To propose and analyze the utilization of explainable artificial intelligence (XAI) techniques for enhancing the interpretability and transparency of IoT botnet detection.
  • To investigate the impact of XAI on model trustworthiness and early detection of emerging botnet patterns.
  • To provide practical guidance for securing IoT ecosystems against botnet threats.

Main Methods:

  • Incorporation of explainable artificial intelligence (XAI) techniques into botnet detection models.
  • Analysis of three XAI methods: rule extraction and distillation, Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanations (SHAP).
  • Experimental evaluation of the proposed XAI-based approach.

Main Results:

  • Experimental results demonstrate the effectiveness of XAI in improving botnet detection interpretability and transparency.
  • XAI techniques provide valuable insights into the inner workings of detection models.
  • The approach facilitates the development of robust defense mechanisms against IoT botnet attacks.

Conclusions:

  • XAI significantly enhances the trustworthiness and transparency of AI/ML-based IoT botnet detection.
  • The study contributes to XAI in cybersecurity research and offers practical insights for securing IoT environments.
  • XAI enables early detection of novel botnet attack patterns.