详情

原文传递 Deepwave: Automated Radio Signal and Protocol Classification Through Deep Learning for Waveform Vulnerability Discovery.

题名：	Deepwave: Automated Radio Signal and Protocol Classification Through Deep Learning for Waveform Vulnerability Discovery.
作者：	Guan, Z; Mastronarde, N.
摘要：	The research objective of this project is to enable a fundamental leap forward in the design, development, evaluation andexperimentation of high-throughput wireless networking with guaranteed security in the presence of adversarial attacks. To thisend, University at Buffalo (UB) and General Electric Aviation Systems (GEAS) propose to i) design new radio signal sensingand protocol classification techniques for automated discovery of the vulnerabilities of wireless systems; ii) simulate UnmannedAerial System (UAS) networks in a contested, degraded, and operationally limited (CDO) environment in the UB’s AirborneNetworking and Communications (UB-ANC) Emulator using SwarmControl for dynamic network management and control ofUAS swarms; and iii) integrate in-phase and quadrature (IQ) sample-level fidelity radio frequency (RF) simulation into theAdvanced Framework for Simulation, Integration, and Modeling (AFSIM) to provide a complete common operating picture forswarm operations.
总页数：	102 pages