| Title | Real Number Signal Processing Can Detect Denial-of-Service Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Boche, Holger, Schaefer, Rafael F., Vincent Poor, H. |
| Conference Name | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Date Published | jun |
| Keywords | Acoustics, algorithmic detection, Blum-Shub-Smale machine, composability, Conferences, denial-of-service attack, digital computers, Metrics, privacy, pubcrawl, real number signal processing, resilience, Resiliency, Signal processing, signal processing security, Turing machines, Wireless communication |
| Abstract | Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Of particular interest are so- called denial-of-service (DoS) attacks in which the jammer is able to completely disrupt the communication. Accordingly, it is of crucial interest for the legitimate users to detect such DoS attacks. Turing machines provide the fundamental limits of today's digital computers and therewith of the traditional signal processing. It has been shown that these are incapable of detecting DoS attacks. This stimulates the question of how powerful the signal processing must be to enable the detection of DoS attacks. This paper investigates the general computation framework of Blum-Shub-Smale machines which allows the processing and storage of arbitrary reals. It is shown that such real number signal processing then enables the detection of DoS attacks. |
| DOI | 10.1109/ICASSP39728.2021.9413911 |
| Citation Key | boche_real_2021 |
Real Number Signal Processing Can Detect Denial-of-Service Attacks