Service and Infrastructure Anomaly Detection (L-ADS and LUCID)
L-ADS (Live Anomaly Detection System) allows the detection of anomalies in the network, that is, cyber-attacks, maintenance notifications, issues out of normality. It works by monitoring the traffic which is entering into FogAtlas and among the microservices and alerts about any abnormal situation. It is integrable with any type of platform.
ATOS has evolved this asset within the DECENTER project which initially was developed in several cybersecurity EU research projects, and now it has been improved in Edge, Fog and Cloud systems.
L-ADS is a high potential asset for cybersecurity environments and has been successfully presented in several workshops and conferences. More information about this asset can be found in the paper, “LADS: A Live Anomaly Detection System based on Machine Learning Methods”.
LUCID (Lightweight, Usable CNN in DDoS Detection) is a lightweight Deep Learning-based DDoS detection framework suitable for online resource-constrained environments, which leverages Convolutional Neural Networks (CNNs) to learn the behaviour of DDoS and benign traffic flows with both low processing overhead and attack detection time. LUCID includes a dataset-agnostic pre-processing mechanism that produces traffic observations consistent with those collected in existing online systems, where the detection algorithms must cope with segments of traffic flows collected over pre-defined time windows.
The source code of LUCID is available at: https://github.com/doriguzzi/lucid-ddos