GBT is Researching the Development of a Machine Learning Driven, RF Cybersecurity System and Protocol

Aiming to provide adaptable, synthesized, new era security for Radio Frequency networks through the use of AI technology

 GBT Technologies is researching the development of a machine learning driven radio frequency (RF) cybersecurity system and protocol. Typical wireless security depends on a software and hardware identification for each wireless device. This fact creates a major cybersecurity vulnerability which can lead to a wireless device’s attack or cloning. GBT is researching the development of a machine learning based system and protocol that will learn to recognize transmitters and receivers based on their unique RF fingerprint. GBT is analyzing a combination of hardware and software to be focused on learning transmitters/receivers RF features, identifying and categorizing their nature as safe or potential malicious attackers. The research is focused on developing a system to identify a potential intruder and then initiating an immediate RF fingerprint change to increase the network’s security measures and also providing an incident response. In response to the out of network suspicious device, the communication system is expected to modify in real time, all other devices fingerprint to transmit in different waveforms, frequencies and other characteristics. The system under development is expected to automatically change all the wireless safe devices natural pattern to block and isolate the intruder. The ultimate goal of the system is to allow an entire network’s security level to be enhanced in real-time to avoid data theft, damage and malicious attacks. This planned system will be learning to synthesize RF waveforms on-the-fly as a response to cyber threat.

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The system and protocol are planned to include AI technology to create an intelligent wireless communication method, maximizing the security of wireless networks by enabling RF spectrum analysis and recognition of RF devices fingerprint and signature. The wireless network under development will constantly monitor and study with the goal of identifying any abnormalities. In case of a possible cyber threat the system is expected to dynamically reconfigure its operating modes to isolate the intrusion and continue its normal, secured operation. GBT aims to equip the system with cognitive, adaptive capabilities in order to perform an automatic reconfiguration, enabling an intelligent, fast response and efficient cybersecurity technology for wireless networks. The system is planned to be autonomous and self-learning to increase RF networks cyber threats awareness, detection, identification and elimination.

“It is GBT’s goal to provide a new level of cybersecurity for wireless networks. Based on our experience in the RF domain we decided to develop an intelligent security system and protocol with the goal of being able to pick out and distinguish between RF signals, identifying the network members fingerprint and identifying the signals that do not belong. As our world’s RF spectrum is becoming highly occupied by numerous radio signals it becomes a great challenge to detect and identify intruders and attackers. Particularly with the rapid, constant expansion of the internet of things (IoT) technology, there now exists a vast amount of RF data in a wide variety of fields. We identified the need for a higher level of cybersecurity in the RF domain to properly secure data, avoid damages and preventing malicious attacks. We are working on an intelligent RF cybersecurity system and protocol that collects important, relevant information about the network’s devices, learning the network’s patterns and provides an immediate response in case of suspicious fingerprints identification.

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The system will have RF awareness to the wireless network patterns, signatures, and characteristics. It will have cognitive capabilities to learn the features of each transmitter and receiver with the goal of providing immediate identification of out-of-networks intruding devices. It will have reasoning algorithms to reach rapid conclusions and to execute RF characteristics change within the network’s members fingerprints and features. We are attempting to develop a system that will learn to understand the difference between safe and harmful signals detected in large bandwidths with the goal of building an efficient, intelligent network security awareness. We strongly believe that particularly in our times a robust RF cybersecurity technology must come of age to protect our normal daily lives, privacy and ultimately national security,” stated Danny Rittman, the Company’s CTO.

There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched and fully developed, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.

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