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Enhanced Cyber threat intelligence in IOT enabled maritime transportation systems through deep learning-based modelling and Identification

  • Unique Paper ID: 166660
  • Volume: 11
  • Issue: 2
  • PageNo: 1489-1498
  • Abstract:
  • The proliferation of IoT technologies in the maritime sector has revolutionized Maritime Transportation Systems (MTS), enabling seamless communication between smart maritime objects and associated infrastructure. However, this interconnectedness exposes MTS to cyber threats, underscoring the need for advanced security measures. Traditional CTI-based solutions often suffer from low detection rates and high false alarms, highlighting the necessity for innovative approaches. The primary objective of this project is to develop an automated framework, DLTIF, to enhance the security of IoT-enabled MTS. DLTIF aims to address the limitations of existing CTI-based solutions by employing deep learning techniques for threat detection and identification. Specifically, the framework focuses on improving detection accuracy, reducing false alarms, and providing early warning of cyber threats. The proposed DLTIF framework demonstrates promising results, achieving up to 99% accuracy in threat detection. Through rigorous evaluation and comparison with traditional and state-of-the-art approaches, DLTIF consistently outperforms existing methods, highlighting its effectiveness in enhancing the security posture of IoT-enabled MTS. And also added CNN and ensemble methods, like CNN+LSTM and Stacking Classifier (RF+MLP+LGBM), are incorporated for boosting accuracy and robustness. Stacking Classifier's impressive 100% accuracy validates ensemble approaches. Additionally, a Flask-based interface streamlines user testing, with built-in authentication ensuring security and access control. This broadens project capabilities with advanced modeling techniques and user-friendly implementation.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 1489-1498

Enhanced Cyber threat intelligence in IOT enabled maritime transportation systems through deep learning-based modelling and Identification

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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