Enhanced Cyber threat intelligence in IOT enabled maritime transportation systems through deep learning-based modelling and Identification
Author(s):
U.Madhava Krishna, Dr. V. Uma Rani
Keywords:
Cyber-attack, cyber threat intelligence, deep learning, the Internet of Things (IoT), maritime transportation systems (MTS), threat type identification.
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.
Article Details
Unique Paper ID: 166660

Publication Volume & Issue: Volume 11, Issue 2

Page(s): 1489 - 1498
Article Preview & Download


Share This Article

Join our RMS



Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Latest Publication

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers