Advanced Machine Learning Techniques for Predicting a Student's Performance in A University
Keywords:
Cyber Security, Cipher text, AES, Private Key, AV.Abstract
The Internet of Things has a big influence on the transportation industry (IoT). Autonomous vehicles (AVs) are designed to improve a variety of daily activities, such as package delivery, traffic flow, and freight transportation. Aside from being on the ground, AVs may also be in the air or underwater, and they have a wide range of applications. To address this problem, we are employing data transfer to autonomous cars based on cyber security (CS). In this instance, a cloud serves as an intermediary to transmit files to an autonomous vehicle. We use the CS-based Advanced Encryption Standard algorithm to further secure the communication by converting the supplied data into cipher text. The encrypted content may be decrypted using the sender's private key created for that particular AV.
References
- R. Using a Powerful Physics-Based Anomaly Detector to Secure Autonomous Vehicles, Quinonez, J. Giraldo, L. Salazar, E. Bauman, A. Cardenas, and Z. Lin. USENIX Security Symposium's 29th annual (USENIX Security 20). Boston, MA, August 2020.
- M. Masood, L. Khan, and B. Data Mining Applications in Malware Detection, Thuraisingham, CRC Press, 2011.
- Y. Adversarial support vector machine learning by Zhou, M. Kantarcioglu, B. M. Thuraisingham, and B. Xi. 2012 ACM KDD: 1059–1067.
- B. SecAI: Integrating Cyber Security and Artificial Intelligence with Applications in Internet of Transportation and Infrastructures, M. Thuraisingham, Annual Conference, Clemson University Center for Connected Multimodal Mobility, October 2019.
- B. Big Data Analytics with Applications in Insider Threat Detection, M. Thuraisingham, P. Pallabi, M. Masud, and L. Khan, CRC Press, 2017.
- K. Exploiting an antiviral interface by W. Hamlen, V. Mohan, M. M. Masud, L. Khan, and B. M. Thuraisingham. Comput. Stand. 31(6):1182–1189 Interfaces (2009).
- L. The usefulness of perturbation-based privacy-preserving data mining for real-world data is discussed by Liu, M. Kantarcioglu, and B. M. Thuraisingham. Info Knowl Eng. 65(1): 5-21 (2008).
- B. Towards a Privacy-Aware Quantified Self Data Management Framework by M. Thuraisingham, M. Kantarcioglu, E. Bertino, J. Z. Bakdash, and M. Fernández. 2018 SACMAT, pp. 173–184
- K. Security Issues for Cloud Computing by W. Hamlen, M. Kantarcioglu, L. Khan, and B. M. Thuraisingham. IJISP 4(2): 36-48 (2010).
- Y. Multistream Classification for Cyber Threat Data with Heterogeneous Feature Space by Li, Y. Gao, G. Ayoade, H. Tao, L. Khan, and B. M. Thuraisingham. WWW, pp 2992-2998, 2019.
- H. "Deep Residual Learning based Enhanced JPEG Compression in the Internet of Things," by Qiu, Q. Zheng, G. Memmi, J. Lu, M. Qiu, and B. M. Thuraisingham, accepted by IEEE Transactions on Industrial Informatics, 2020.
- G. Decentralized IoT Data Management Using BlockChain and Trusted Execution Environment by Ayoade, V. Karande, L. Khan, and K. W. Hamlen. IRI, pages 15-22, 2018.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST

This work is licensed under a Creative Commons Attribution 4.0 International License.