A Multiple Classification Method Based on the D-S Evidence Theory

Wang

The Scientific World Journal

2014

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 senor 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.

Section: Optimized Ds Evidence Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Anomaly Detection System with Optimized DS Evidence Theory

Wang

The Scientific World Journal

2014

“…The cloud theory is applied to the classification of remote sensing images [5], which not only improves the uncertainty and fuzziness of classification, but also improves the classification accuracy. Analogously, the cloud model is applied to hyperspectral remote…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Data Classification Based on EMAP and Multi-Scale Cloud Model

Zhao¹,

Bao²

2018

IJSIP

Deep Learning and Neural Networks

A Hybrid NIDS Model Using Artificial Neural Network and D-S Evidence

et al. 2020

Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, can improve the performance of intrusion detection systems. However, for the current network intrusion detection methods, the detection precision, especially for low-frequent attacks, detection stability and training time are still needed to be enhanced. In this paper, a new model which based on optimized BP neural network and Dempster-Shafer theory to solve the above problems and help NIDS to achieve higher detection rate, less false positive rate and stronger stability. The general process of the authors' model is as follows: firstly dividing the main extracted feature into several different feature subsets. Then, based on different feature subsets, different ANN models are trained to build the detection engine. Finally, the D-S evidence theory is employed to integration these results, and obtain the final result. The effectiveness of this method is verified by experimental simulation utilizing KDD Cup1999 dataset.