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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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EXPLAINABLE FEDERATED INTELLIGENCE FOR SECURITY AND PRIVACY IN CONNECTED VEHICLE NETWORKS
Authors
L. SHIVA SHANKER,,NARAYANDAS VAISHNAVI,VENDRU LAKSHMITHULASI,NALLA NANDINI,VEYYA SIRICHANDANA
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Keywords
APT, Connected Vehicles Security, Explainable AI, Federated Learning, Privacy-Preserving Deep
Abstract
: As the use of autonomous and smart vehicles in the ground transportation systems increases, new security issues emerge. That shift from human-run operations to computer-run operations allows bad people to attack more easily. With Internet of Things (IoT) becoming more common in cars, they are constantly making and sharing a lot of data. Sophisticated attackers can exploit the weaknesses of this trend with Advanced Persistent Threats (APT) for example. It is very important to be able to find the APTs in the vehicles that have the IoT technology . We have to do better at detecting these threats. Traditional centralised Machine Learning (ML) techniques are hindered by the critical demand of data privacy in vehicles. In addition, there are no publicly available APT datasets in the vehicle domain, which makes it difficult to develop and test models. That's a big problem for cybersecurity in this ever-changing area. To address these issues, the study introduces a novel Federated Deep Neural Network (FDNN) framework with a privacy-preserving technique. The paper discusses the main challenges in detecting APTs and describes its unique contribution to the field. It discusses the research questions that are driving the study. The datasets of UNSW-NB15, Edge-IIoTset and CSE-CIC-IDS2018 depict different phases of an APT attack. The framework constructed with these datasets is examined and evaluated. The framework without the privacy-preserving technique achieves the APT detection accuracies of 97.32%, 96.81%, and 98.06%, respectively, for these datasets. With the privacy-preserving technique, the accuracies of the framework are 95.62%, 96.11%, and 95.63%, respectively. There are tables with all results and other evaluation metrics like Precision, False Positive Rate and F1 Score. We apply "Shapley Additive Explanations (SHAP)" analysis to our framework to identify the most important features for detecting APTs. This work confirms the effectiveness of a novel framework for APT detection in distributed vehicular environments. The framework works well because it reduces the data and features. This was demonstrated by extensive testing with several benchmark datasets. Future work will investigate the framework's ability to identify APTs in other domains.
IJCRT's Publication Details
Unique Identification Number - IJCRT2402849
Paper ID - 312110
Author type - Indian Author
Page Number(s) - h231-h239
Pubished in - Volume 12 | Issue 2 | February 2024
DOI (Digital Object Identifier) - https://doi.org/10.56975/ijcrt.v12i2.312110
No Of Downloads - 78
Author Country - India, 505236, metrostation, metrostation, 505236, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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L. SHIVA SHANKER,,NARAYANDAS VAISHNAVI,VENDRU LAKSHMITHULASI,NALLA NANDINI,VEYYA SIRICHANDANA, "EXPLAINABLE FEDERATED INTELLIGENCE FOR SECURITY AND PRIVACY IN CONNECTED VEHICLE NETWORKS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 2, pp.h231-h239, February 2024, Available at :http://www.ijcrt.org/papers/IJCRT2402849.pdf
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