Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Authors:
raw_author_name: Senthil Murugan Nagarajan
raw_affiliation_string: Department of Mathematics, University of Luxembourg, 2 Av. de l'Universite, Esch-Sur-Alzette, L-4365, Luxembourg
raw_author_name: Ganesh Gopal Devarajan
raw_affiliation_string: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh 201204, India
raw_author_name: Ramana T.V.
raw_affiliation_string: Department of Computer Science and Engineering, Jain University, Jakkasandra Post, Kanakapura Road, Bengaluru, Karnataka 562112, India
raw_author_name: Asha Jerlin M.
raw_affiliation_string: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Kelambakkam - Vandalur Rd, Rajan Nagar, Chennai, Tamil Nadu 600127, India
raw_author_name: Ali Kashif Bashir
raw_affiliation_string: Department of Computer Science and Mathematics, Lebanese American University, Chouran Beirut, 13-5053, Beirut, Lebanon; Department of Computing and Mathematics, Manchester Metropolitan University, All Saints, All Saints Building, M15 6BH, Manchester, United Kingdom; Woxsen School of Business, Woxsen University, Kamkole, Sadasivpet, Sangareddy District, Telangana 502345, India
raw_author_name: Yasser D. Al-Otaibi
raw_affiliation_string: Department of Information Systems, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

Abstract: Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system's vulnerability comprehensively. To enhance the system's capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system's perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system's ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model's ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.