2022/01/05 by Makan Zamanipour, Zamanipour, Makan
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Signal Processing (eess.SP) #Wireless Communication Security Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.01842
openalex publication_date 2022/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When an adversary gets access to the data sample in the adversarial robustness models and can make data-dependent changes, how has the decision maker consequently, relying deeply upon the adversarially-modified data, to make statistical inference? How can the resilience and elasticity of the network be literally justified - if there exists a tool to measure the aforementioned elasticity? The principle of byzantine resilience distributed hypothesis testing (BRDHT) is considered in this paper for cognitive radio networks (CRNs) - without-loss-of-generality, something that can be extended to any type of homogeneous or heterogeneous networks - while the byzantine primary user (PU) has a signal-to-noise-ratio (SNR) including the coefficient of \fracdℓ ( θ| \mathscrs0 )dℓ ( θ ) which is in relation to the temporal rate of the α-leakage as the appropriate tool to measure the aforementioned resilience. Our novel online algorithm - which is named \mathbbOBRDHT - and solution are both unique and generic over which an evaluation is finally performed by simulations - e.g. an evaluation of the total error as the false alarm probability in addition to the miss detection probability versus the sensing time.