Advanced Modular Unsupervised Network Countering Emerging Hybrid Threats
Development of integrated solutions based on emerging and disruptive technologies such as artificial intelligence.
Development of Framework for detect, deter and response hybrid threats
AMUNET PROJECT (Advanced Modular Unsupervised Network Countering Emerging Hybrid Threats) aims at the creation of a series of advanced tools, frameworks and scientific papers that will advance the early detection of the actions of the most vulnerable networks.
Main objective of this project initiative is to obtain a development of the early detection of threat actors of hybrid threats in the cyberspace through the identification of tactics, techniques and strategies of threat actors associated with the phase of action in which they are.
Based on phase of action in which the threat action is found and their relationships to the objectives of influence, destabilization, compromise or any other potential engagement or any other potential impact expected from their actions is identified, qualified and prepare for deterrence.
For this purpose, a multidisciplinary team develops several lines of action in specific fields of action of detection, hybrid threats and their potential influences in cyberspace.
AMUNET PROJECT responds to an emerging need in terms of anticipating to an emerging need in terms of anticipating actions in the gray zone of operations and hybrid conflicts through the development of intelligence capabilities to detect intelligence capabilities that allow the early detection of indicators associated with the activities of hybrid actors with establishment of a unified framework that will allow the harmonization of information for exploitation in real time activities
This research project does not intend to perform any exploitation of classified information but aims to bring scientific conceptual models for development of real frameworks for countering hybrid threats by the different European agencies countering hybrid threats in their different approaches.
In this sense, AMUNET PROJECT is an independent initiative of a scientific and technological nature.
This initiative is not aligned with any interest, ideology or dependence outside the purely scientific and
of technological capabilities for the early detection and automated decision making frameworks of threats detection/response/deterence/defense taking in consideration open source information processing and early detection of threats, real time information processing, artificial intelligence and other artificial intelligence capabilities , among other lines of scientific action and exploration of the applicability in the scenario of hybrid conflicts.
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Amunet is known as "the god of mystery" in Egyptian mythology. Amunet personifies the north wind, the one who brings life and reveals chaos and darkness.
She is part of the Egyptian gods who were self-begotten, her name referred to the occult, however, the shadow of Amunet symbolized protection, and together with her husband Amun embodied the enigmatic, where chaos and darkness resided.
Amunet was a goddess who represented the wind, she could not be touched but could be felt.
She was also known as the mother who is father, since she was the as the mother who is the father, since she did not need any husband, as she had the power of creation the power of creation without the help of the male gender.
An example of a conceptual model under study by the AMUNET team is the one developed by Giannopoulos, G., Smith, H., Theocharidou, M., The Landscape of Hybrid Threats: A conceptual model, European Commission, Ispra, 2020, PUBSY No. 123305 which provides sufficient conclusions from an analysis point of view of the mechanisms and tools used by threat actors in the hybrid conflict scenario. A
However, the AMUNET team is working on the documentary analysis of other sources whose conclusions will be presented in different scientific publications and which can be found in the publications of our project.
We use the most innovative real time artificial intelligence capabilities to improve detection, deterrence and attribution of hybrid threat actors.
Our supervised and unsupervised modules are submitted to continuous improvement based on last disruptive innovation in technology.