Intrusion Detection Using Federated Learning for Computing  

作　　者：R.S.Aashmi T.Jaya 

机构地区：[1]Department of Computer Science and Engineering,CSI Institute of Technology,Thovalai,Tamilnadu,India [2]Department of Electronic Communications and Engineering,CSI Institute of Technology,Thovalai,Tamilnadu,India

出　　处：《Computer Systems Science & Engineering》2023年第5期1295-1308,共14页计算机系统科学与工程（英文）

摘　　要：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%.

关 键 词：Jungle computing high performance computation federated learning false positive rate intrusion detection system(IDS) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]