Enterprise Europe Network (Profile: TRES20200320001)
The Spanish branch of a multinational IT company is looking for a technology that enables to apply unsupervised ML (Machine Learning) models to a large stack of cyber-data collected with traditional net-logging systems and characterise a large variety of cyber-attacks, reaching the ability to identify new types of unclassified attacking methodologies. SMEs are sought for technical cooperation agreements.
A large IT company with offices in Spain is looking for a system for anomaly detection in cyber network data using Artificial Intelligence (AI) models.
As the amount of cyber data continues to grow, cyber network defenders are faced with increasing amounts of data they must analyze to ensure the security of their networks. In addition, new types of attacks are constantly being created and executed globally.
Current rules-based approaches are effective at characterizing and flagging known attacks, but they typically fail when presented with a new attack or new types of data.
By comparison, unsupervised machine learning (ML) offers distinct advantages by not requiring labeled data to learn from large amounts of network traffic.
Intrusion detection techniques usually make the assumption that intrusions should be anomalous relative to the network as a whole. ML could move the focus to the analysis on flowtype data, bringing numerous gain in terms of the adaptability and responsiveness in malicious attacks detection.
The idea is to apply unsupervised ML models to a large stack of cyber-data collected with traditional net-logging systems and characterize a large variety of cyber-attacks, reaching the ability to identify new types of unclassified attacking methodologies.
The company is looking for SMEs able to offer a solution for the above described problem under a technical cooperation agreement.
For more information please contact Enterprise Europe Network Latvia.