Cognitive Threat Detection Systems for Autonomous Vehicle Networks

Abstract

The deep neural system is practicable entity for a variety of standalone pattern recognition and classification systems that use low complexity in the case detection task and largely accounts for the state-of-the-art in effectively many real-world problems. Another requirement of the different AI-powered classes based on the learning techniques are participating into focused conditional phrases and serves against learning models. The accidental driver, driver assistance, and automated vehicles will expect to allow secure cooperation against the communication and decision making for safeguarding the individual and society and promote cue and environmentally friendly driving. The physical (and nonphysical) addresses of these threat signals give precautions against the smart engineers and professionals to realistically design an entire protected ecosystem in the automatic vehicle subsystems. The core of this survey article is based to focus on the state-of-the-art in autonomous vehicle and connected drone security mechanisms and their key assessment through this document, such as 6G technologies and systems and application assignment, and the prohibiting the cyber-physical impacts between incoming dives in local and public domain performances.