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XRecon: An Explainbale IoT Reconnaissance Attack Detection System Based on Ensemble Learning.

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This study presents a machine learning system to detect reconnaissance attacks on Internet of Things (IoT) devices. The efficient system achieves 99% accuracy, protecting vulnerable IoT devices from early-stage cyber threats.

Keywords:
IoTXAIattackdetectionmachine learningreconnaissance

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT) Security

Background:

  • The proliferation of Internet of Things (IoT) devices has created a vast attack surface for malicious actors.
  • Reconnaissance attacks, including scanning and information gathering, are precursors to more severe threats like botnets and malware injection.
  • Existing security measures may not be sufficient for resource-constrained IoT environments.

Purpose of the Study:

  • To introduce a novel machine learning-based detection system for reconnaissance attacks targeting IoT devices.
  • To develop an explainable ensemble model capable of identifying early-stage attack activities.
  • To design a lightweight and efficient system suitable for resource-limited IoT ecosystems.

Main Methods:

  • Development of an explainable ensemble machine learning model.
  • Implementation of a system focused on detecting scanning and reconnaissance activities.
  • Testing the system's performance in terms of accuracy, false positive, and false negative rates.

Main Results:

  • The proposed system achieved a high accuracy rate of 99%.
  • Demonstrated exceptionally low false positive (0.6%) and false negative (0.05%) rates.
  • The system proved to be efficient with low resource consumption, suitable for constrained environments.

Conclusions:

  • The developed machine learning system effectively detects reconnaissance attacks on IoT devices.
  • The system's explainability and efficiency make it a valuable tool for early-stage IoT threat mitigation.
  • This approach offers a promising solution for enhancing the security of the rapidly growing IoT ecosystem.