| Title | Random-Byzantine Attack Mitigation in Cognitive Radio Networks using a Multi-Hidden Markov Model System |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Taggu, Amar, Marchang, Ningrinla |
| Conference Name | 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA) |
| Keywords | byzantine attacks, Cognitive radio, cognitive radio networks, Cognitive Radio Security, Cognitive Radios, CRN, Hidden Markov models, HMM, multiHidden Markov Model system, primary user emulation attack, pubcrawl, radio spectrum management, radiofrequency interference, random-Byzantine Attack mitigation, resilience, Resiliency, secondary users, signal detection, telecommunication security, television frequency spectrum, white space, wireless channels |
| Abstract | Cognitive Radio Networks (CRN) are opportunistic networks which aim to harness the white space in the television frequency spectrum, on a need-to-need basis, without interfering the incumbent, called the Primary User (PU). Cognitive radios (CR) that sense the spectrum periodically for sensing the PU activity, are called Secondary Users (SU). CRNs are susceptible to two major attacks, Byzantine attacks and Primary User Emulation Attack (PUEA). Both the attacks are capable of rendering a CRN useless, by either interfering with the PU itself or capturing the entire channel for themselves. Byzantine attacks detection and mitigation is an important security issue in CRN. Hence, the current work proposes using a multi-Hidden Markov Model system with an aim to detect different types of random-Byzantine attacks. Simulation results show good detection rate across all the attacks. |
| DOI | 10.1109/ICECTA48151.2019.8959766 |
| Citation Key | taggu_random-byzantine_2019 |
Random-Byzantine Attack Mitigation in Cognitive Radio Networks using a Multi-Hidden Markov Model System