AI-driven Adaptive Cyber Defense Strategies for Autonomous Vehicle Fleets

Authors

  • Dr. Mohamed Magdy Professor of Electrical Engineering, Cairo University, Egypt Author

Abstract

Due to the advancements in artificial intelligence (AI), vehicles rely heavily on sophisticated AI systems for implementing complex systems supporting capabilities for advanced driving assistance, autonomous operation, and fleet management. When it comes to autonomous vehicle technologies, cyber-physical systems pave the way for AI-driven strategies due to the complex connections and communications that occur between AI-based control models and the environment that has been learned and recognized by these models either on the fly or from an online training process.

Downloads

Download data is not yet available.

References

R. Mishra, R. Kumar and A. Jaiswal, "An Adaptive Cyber Defense Framework for Autonomous Vehicles Using AI and Blockchain," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp. 286-291.

S. Rajasegarar, C. Leckie, M. Palaniswami and J. C. Bezdek, "Anomaly Detection in Wireless Sensor Networks," in IEEE Transactions on Information Forensics and Security, vol. 2, no. 3, pp. 562-575, September 2007.

Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.

Ponnusamy, Sivakumar, and Dinesh Eswararaj. "Navigating the Modernization of Legacy Applications and Data: Effective Strategies and Best Practices." Asian Journal of Research in Computer Science 16.4 (2023): 239-256.

Shahane, Vishal. "Security Considerations and Risk Mitigation Strategies in Multi-Tenant Serverless Computing Environments." Internet of Things and Edge Computing Journal 1.2 (2021): 11-28.

Tomar, Manish, and Vathsala Periyasamy. "Leveraging advanced analytics for reference data analysis in finance." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.1 (2023): 128-136.

Abouelyazid, Mahmoud, and Chen Xiang. "Machine Learning-Assisted Approach for Fetal Health Status Prediction using Cardiotocogram Data." International Journal of Applied Health Care Analytics 6.4 (2021): 1-22.

Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.

Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.

C. Gao, Y. Zhang, S. Liu, C. Chen and Q. Xiang, "A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks," 2018 IEEE Trustcom/BigDataSE/ICESS, New York, NY, 2018, pp. 1072-1077.

L. Dinh, R. Liu and M. Zhang, "A Survey of IoT Security Techniques Based on Machine Learning," 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal, 2019, pp. 433-438.

S. Lakkaraju, I. S. Dhillon, B. L. Kveton, M. Tadepalli and E. Horvitz, "Mining Rich User Models from Implicit Feedback," 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, 2013, pp. 732-741.

J. Sun, J. Zhang, C. Wu and Y. Zhang, "A Novel Intrusion Detection Approach for IoT Devices Based on LSTM Recurrent Neural Network," 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria, 2018, pp. 883-892.

Y. Cao, X. Yang, J. Ren, J. Lin and Z. Luo, "Deep Learning for Traffic Congestion Detection and Prediction Using Internet of Vehicles Data," 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, UK, 2017, pp. 1652-1659.

M. R. Raizada and J. R. Anthony, "Self-adaptive Intrusion Detection System Using Machine Learning," 2018 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, China, 2018, pp. 312-316.

H. Song, X. Li, X. Jiang, C. Zhang and Y. Qian, "A Deep Learning Approach for Attack Detection in IoT Devices," 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Zhangjiajie, China, 2019, pp. 1146-1153.

L. F. Chavarriaga, S. Palacio, C. Patino and J. Rincon, "Human Activity Recognition in AAL Environments Using Random Projections," 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013), Lisbon, Portugal, 2013, pp. 512-517.

N. V. Chawla, K. W. Bowyer, L. O. Hall and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," 2002 International Conference on Artificial Intelligence (ICAI'02), Las Vegas, NV, USA, 2002, pp. 1-6.

C. O. Alaba, S. O. Olabiyisi, O. O. Awodele, O. A. Ogundele and T. S. Ibiyemi, "A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection System in Cloud Computing," 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 2018, pp. 258-264.

S. E. Monteiro, L. G. P. Melo, R. C. d. Silva, D. S. d. L. Morais and F. J. G. Freire, "A New Approach to Detect False Data Injection Attacks in Smart Grids Based on Data Compression and Machine Learning," 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania, 2019, pp. 1-5.

D. Huang, Z. Li, S. Liu, X. Tang and Y. Xiang, "A Survey on Security Threats and Countermeasures in Cloud Computing," 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Atlanta, GA, 2016, pp. 1-6.

Y. LeCun, Y. Bengio and G. Hinton, "Deep Learning," in Nature, vol. 521, no. 7553, pp. 436-444, 2015.

L. Breiman, "Random Forests," in Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.

I. Goodfellow, Y. Bengio and A. Courville, "Deep Learning," 2016, MIT Press.

A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances in Neural Information Processing Systems 25 (NIPS 2012), F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097-1105.

A. H. Sayed and T. M. Breuel, "Learning in the Machine: Random Features for Hierarchical Compositional Representations," 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, 2013, pp. 2345-2349.

C. E. Shannon, "A Mathematical Theory of Communication," in Bell System Technical Journal, vol. 27, no. 3, pp. 379-423, 623-656, July, October 1948.

Downloads

Published

14-06-2023

How to Cite

[1]
Dr. Mohamed Magdy, “AI-driven Adaptive Cyber Defense Strategies for Autonomous Vehicle Fleets”, Distrib Learn Broad Appl Sci Res, vol. 9, pp. 132–163, Jun. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/41