As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ability.However,ELM with a single hidden layer structure often fails to achieve good results when faced with large...As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ability.However,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi-featured problems.To resolve this problem,we propose a multi-layer framework for the ELM learning algorithm to improve the model’s generalization ability.Moreover,noises or abnormal points often exist in practical applications,and they result in the inability to obtain clean training data.The generalization ability of the original ELM decreases under such circumstances.To address this issue,we add model bias and variance to the loss function so that the model gains the ability to minimize model bias and model variance,thus reducing the influence of noise signals.A new robust multi-layer algorithm called ML-RELM is proposed to enhance outlier robustness in complex datasets.Simulation results show that the method has high generalization ability and strong robustness to noise.展开更多
With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different lev...With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network's performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete(or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4 G LTE-A to detect network's anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.展开更多
The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors.Firstly,a new algorithm in this paper named danger-theory...The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors.Firstly,a new algorithm in this paper named danger-theory-based abnormal traffic detection was presented.Then an advanced ID3 algorithm was presented to classify the abnormal traffic.Finally a new model of anomaly traffic detection was built upon the two algorithms above and the detection results were integrated with firewall.The firewall limits the bandwidth based on different types of abnormal traffic.Experiments show the outstanding performance of the proposed approach in real-time property,high detection rate,and unsupervised learning.展开更多
基金Project(21878081)supported by the National Natural Science Foundation of ChinaProject(222201917006)supported by the Fundamental Research Funds for the Central Universities,China。
文摘As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ability.However,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi-featured problems.To resolve this problem,we propose a multi-layer framework for the ELM learning algorithm to improve the model’s generalization ability.Moreover,noises or abnormal points often exist in practical applications,and they result in the inability to obtain clean training data.The generalization ability of the original ELM decreases under such circumstances.To address this issue,we add model bias and variance to the loss function so that the model gains the ability to minimize model bias and model variance,thus reducing the influence of noise signals.A new robust multi-layer algorithm called ML-RELM is proposed to enhance outlier robustness in complex datasets.Simulation results show that the method has high generalization ability and strong robustness to noise.
基金supported in part by the National Natural Science Foundation of China under the Grants No.61431011 and 61671371the National Science and Technology Major Project under Grant no.2016ZX03001016-005+1 种基金the Key Research and Development Program of Shaanxi Province under Grant No.2017ZDXM-G-Y-012the Fundamental Research Funds for the Central Universities
文摘With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network's performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete(or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4 G LTE-A to detect network's anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.
基金Shanghai Education Commission Foundation for Excellent Young High Education Teachers,China(No.xqz05001No.YYY-07008)
文摘The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors.Firstly,a new algorithm in this paper named danger-theory-based abnormal traffic detection was presented.Then an advanced ID3 algorithm was presented to classify the abnormal traffic.Finally a new model of anomaly traffic detection was built upon the two algorithms above and the detection results were integrated with firewall.The firewall limits the bandwidth based on different types of abnormal traffic.Experiments show the outstanding performance of the proposed approach in real-time property,high detection rate,and unsupervised learning.
