Artificial Intelligence Supported Detection Systems on Embedded Devices

Abstract

With the transition to the information society, all areas of our lives are rapidly shifting to the digital environment. From education to health, from citizenship procedures to social life, all areas of our lives are interacting in the digital cyber environment. In this process, smart cities, smart networks, and smart factories, especially critical infrastructures required for social life, have become open to the intranet and then to the internet for reasons such as efficient efficiency, speed, remote maintenance, and maintenance. Along with this process, these systems have faced new threat surfaces. One of the components that play an essential role in the operation of these systems is embedded systems. These systems contribute significantly to the effective operation of essential infrastructures. However, any interruption in these systems can lead to significant negative consequences, including economic damage and human life. Although there are many studies on the functioning of embedded systems, there are not enough studies on the cyber security analysis of these systems. For this reason, in this study, attack and detection analyses for embedded systems have been carried out on the test environment created using real systems. The study aims to detect passive attack, which is more difficult to detect than active attacks on the system, by using artificial intelligence algorithms. The analysis results have shown that the attack has been detected in a high ratio. It has been evaluated that the study will significantly contribute to other studies on the security of embedded systems.

Keywords

• Embedded System Security
• Vulnerability Analysis
• Artificial Intelligence
• Cyber Security

References

1. [1] M. Jiménez, R. Palomera, and I. Couvertier. Introduction to Embedded Systems. Springer, New York, NY, 2014.
2. [2] D. N. Serpanos and A. G. Voyiatzis. Security challenges in embedded systems. ACM Transactions on Embedded Computing Systems (TECS), 12(1s):1–10, 2013.
3. [3] A. Farmahini-Farahani, S. Vakili, S. M. Fakhraie, S. Safari, and C. Lucas. Parallel scalable hardware implementation of asynchronous discrete particle swarm optimization. Engineering Applications of Artificial Intelligence, 23(2):177–187, 2010.
4. [4] P. K. Muhuri, A. K. Shukla, and A. Abraham. Industry 4.0: A bibliometric analysis and detailed overview. Engineering applications of artificial intelligence, 78:218–235, 2019.
5. [5] M. Keefe. Timeline: Critical infrastructure attacks increase steadily in past decade. Computerworld, 5, 2012.
6. [6] L. Apa and C. M. Penagos. Compromising industrial facilities from 40 miles away. IOActive Technical White Paper, 2013.
7. [7] D. Papp, Z. Ma, and L. Buttyan. Embedded systems security: Threats, vulnerabilities, and attack taxonomy. In 2015 13th Annual Conference on Privacy, Security and Trust (PST), pages 145–152, 2015.
8. [8] L. C. De Silva, C. Morikawa, and I. M. Petra. State of the art of smart homes. Engineering Applications of Artificial Intelligence, 25(7):1313–1321, 2012.

9. [9] F. Molazem Tabrizi. Security analysis and intrusion detection for embedded systems: a case study of smart meters. PhD thesis, Electrical and Computer Engineering, University of British Columbia, 2017.
10. [10] M. H. Özcanhan. Security and reliability in embedded systems. PhD thesis, Computer Engineering and Computer Science and Control, Dokuz Eylul University, 2011.
11. [11] G. Sungur and B. Barış. Labview and embedded system based new iot solution for industrial applications. Academic Platform Journal of Engineering and Smart Systems, 10(2):106–114, 2022.
12. [12] A. Arış, S. F. Oktuğ, and S. B. Ö. Yalçın. Internet-of-things security: Denial of service attacks. In 2015 23nd Signal Processing and Communications Applications Conference (SIU), pages 903–906, 2015.
13. [13] S. Ni, Y. Zhuang, J. Gu, and Y. Huo. A formal model and risk assessment method for security-critical real-time embedded systems. Computers & Security, 58:199–215, 2016.