Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

A framework for preventing unauthorized drone intrusions through radar detection and GPS spoofing

The increasing use of Global Positioning System (GPS)-based autonomous drones in various civilian and military applications has raised concerns about malicious or unintentionally harmful activities that can be carried through them. It is necessary to detect these intruding drones within protected areas and prevent their unauthorized access by denying them entry. We propose a framework that combines the detection of intruding drones using an L-band radar and then counters by transmitting fake GPS coordinates toward the drones, effectively redirecting them. This article explains the setup required to add to an existing monostatic radar that provides two-dimensional information, i.e., range and azimuth information, to enable the proposed setup to get the elevation angle of the drone. We propose a linear array design using digital receive-only beamforming techniques in the elevation domain to compute the elevation angle in addition to the range, velocity, and azimuth information being provided by the monostatic radar to get complete information about the intruding drone. The simulation of drone detection is followed by an examination of the impact of transmitting fabricated GPS coordinates to the drone. Experimental verification has been conducted to validate both the digital beamforming algorithm and the spoofing technique. This approach blocks the reception of actual GPS signals in the drone and replaces the drone's GPS coordinates with alternative, desired coordinates. The proposed framework can be used to prevent unauthorized drone intrusions in the protected area