Intrusion Detection Using Federated Learning for Computing

The integration of clusters, grids, clouds, edges and other computing platforms result in contemporary technology of jungle computing. This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time. Federated learning is a collaborative machine learning approach without centralized training data. The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior, potentially caused by malicious adversaries and it can emerge with new and unknown attacks. The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service. Moreover, the updated system model is send to the centralized server in jungle computing, to detect their pattern. Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors. In our proposed work, we have implemented an intrusion detection system that has high accuracy, low False Positive Rate (FPR) scalable, and versatile for the jungle computing environment. The execution time taken to complete a round is less than two seconds, with an accuracy rate of 96%.