Linyuan Zhang

TL;DR: In this article, the authors propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance, and provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where an important event is being held and only authorized drones are allowed to fly over.

TL;DR: This paper proposes a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks, and provides an in-depth tutorial on the state-of-the-art Byzantine defense schemes.

TL;DR: A data cleansing-based robust spectrum sensing algorithm is developed to solve the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors, where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious makes the existing cooperative sensing schemes ineffective.

TL;DR: This paper proposes a robust spectrum sensing scheme by developing a data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of non zero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data.

TL;DR: This paper proposes and analyzes a novel probabilistic soft SSDF attack model, which goes beyond the existing models for its generalization, and finds an interesting trade-off between destructiveness and stealthiness.