title: Network anomaly detection system with optimized DS evidence theory.
abstract: Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.
authors.affiliation: School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China.
authors.forename: Kaiyu
authors.initials: K
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003195:Computer Communication Networks / Q000592:standards*; D016494:Computer Security / Q000592:standards*; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D011336:Probability; D015203:Reproducibility of Results