Detection Schemes of Illegal Spectrum Access Behaviors in Multiple Authorized Users Scenario

Author(s):  
Hao Fang ◽  
Tao Zhang ◽  
Yueming Cai ◽  
Linyuan Zhang ◽  
Hao Wu
2009 ◽  
Vol E92-B (12) ◽  
pp. 3581-3585
Author(s):  
Hyoungsuk JEON ◽  
Sooyeol IM ◽  
Youmin KIM ◽  
Seunghee KIM ◽  
Jinup KIM ◽  
...  

Author(s):  
A. L. Stempkovskiy ◽  
◽  
D. V. Telpukhov ◽  
A. I. Demeneva ◽  
T. D. Zhukova ◽  
...  

2020 ◽  
Vol 16 (2) ◽  
pp. 280-289
Author(s):  
Ghalib H. Alshammri ◽  
Walid K. M. Ahmed ◽  
Victor B. Lawrence

Background: The architecture and sequential learning rule-based underlying ARFIS (adaptive-receiver-based fuzzy inference system) are proposed to estimate and predict the adaptive threshold-based detection scheme for diffusion-based molecular communication (DMC). Method: The proposed system forwards an estimate of the received bits based on the current molecular cumulative concentration, which is derived using sequential training-based principle with weight and bias and an input-output mapping based on both human knowledge in the form of fuzzy IFTHEN rules. The ARFIS architecture is employed to model nonlinear molecular communication to predict the received bits over time series. Result: This procedure is suitable for binary On-OFF-Keying (Book signaling), where the receiver bio-nanomachine (Rx Bio-NM) adapts the 1/0-bit detection threshold based on all previous received molecular cumulative concentrations to alleviate the inter-symbol interference (ISI) problem and reception noise. Conclusion: Theoretical and simulation results show the improvement in diffusion-based molecular throughput and the optimal number of molecules in transmission. Furthermore, the performance evaluation in various noisy channel sources shows promising improvement in the un-coded bit error rate (BER) compared with other threshold-based detection schemes in the literature.


Author(s):  
Shanghao Shi ◽  
Yang Xiao ◽  
Wenjing Lou ◽  
Chonggang Wang ◽  
Xu Li ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3257
Author(s):  
Arne Bochem ◽  
Benjamin Leiding

Today, increasing Internet of Things devices are deployed, and the field of applications for decentralized, self-organizing networks keeps growing. The growth also makes these systems more attractive to attackers. Sybil attacks are a common issue, especially in decentralized networks and networks that are deployed in scenarios with irregular or unreliable Internet connectivity. The lack of a central authority that can be contacted at any time allows attackers to introduce arbitrary amounts of nodes into the network and manipulate its behavior according to the attacker’s goals, by posing as a majority participant. Depending on the structure of the network, employing Sybil node detection schemes may be difficult, and low powered Internet of Things devices are usually unable to perform impactful amounts of work for proof-of-work based schemes. In this paper, we present Rechained, a scheme that monetarily disincentivizes the creation of Sybil identities for networks that can operate with intermittent or no Internet connectivity. We introduce a new revocation mechanism for identities, tie them into the concepts of self-sovereign identities, and decentralized identifiers. Case-studies are used to discuss upper- and lower-bounds for the costs of Sybil identities and, therefore, the provided security level. Furthermore, we formalize the protocol using Colored Petri Nets to analyze its correctness and suitability. Proof-of-concept implementations are used to evaluate the performance of our scheme on low powered hardware as it might be found in Internet of Things applications.


2021 ◽  
pp. 2104879
Author(s):  
Carmelita Rodà ◽  
Mauro Fasoli ◽  
Matteo L. Zaffalon ◽  
Francesca Cova ◽  
Valerio Pinchetti ◽  
...  

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