A New Method of Network Data Link Troubleshooting

2005 ◽  
pp. 890-900
Author(s):  
Qian-Mu Li ◽  
Yong Qi ◽  
Man-Wu Xu ◽  
Feng-Yu Liu
Keyword(s):  
New Method ◽  
Network Data ◽  
Data Link

Enhancing link prediction by exploring community membership of nodes

2019 ◽  
Vol 33 (31) ◽  
pp. 1950382
Author(s):  
Shenshen Bai ◽  
Shiyu Fang ◽  
Longjie Li ◽  
Rui Liu ◽  
Xiaoyun Chen
Keyword(s):  
Community Structure ◽  
Prediction Model ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Network Data ◽  
Data Link ◽  
Structure Information ◽  
Community Membership

With the proliferation of available network data, link prediction has become increasingly important and captured growing attention from various disciplines. To enhance the prediction accuracy by making full use of community structure information, this paper proposes a new link prediction model, namely CMS, in which different community memberships of nodes are investigated. In the opinion of CMS, different memberships can have different influence to link’s formation. To estimate the connection likelihood between two nodes, the CMS model weights the contribution of each shared neighbor according to the corresponding community membership. Three CMS-based methods are derived by introducing three forms of contribution that neighbors make. Extensive experiments on 12 networks are conducted to evaluate the performance of CMS-based methods. The results manifest that CMS-based methods are more effective and robust than baselines.

Detection of Network Intrusion Signal in Deep Camouflage Based on Chaotic Synchronization

2013 ◽  
Vol 380-384 ◽  
pp. 2695-2698
Author(s):  
Cai Tian Zhang ◽  
Yi Bo Zhang
Keyword(s):  
Signal Detection ◽  
Expectation Maximization ◽  
Detection Method ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Chaotic Synchronization ◽  
New Method ◽  
Network Data ◽  
Network Intrusion ◽  
Better Than

For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.

scholarly journals WHOI SDSL Data-Link Project—Ethernet Telemetry through Sea Cables

2017 ◽  
Vol 34 (2) ◽  
pp. 269-275 ◽  
Author(s):  
Marshall Swartz ◽  
Daniel J. Torres ◽  
Steve Liberatore ◽  
Robert Millard
Keyword(s):  
Fold Increase ◽  
New Method ◽  
Research Vessel ◽  
Communications Technology ◽  
Minimal Cost ◽  
Digital Subscriber Line ◽  
Data Link ◽  
Scientific Instrumentation ◽  
Subscriber Line

AbstractA data telemetry technique for communicating over standard oceanographic sea cables that achieves a nearly 100-fold increase in bandwidth as compared to traditional systems has been recently developed and successfully used at sea on board two Research Vessel (R/V) Atlantis cruises with an 8.5-km, 0.322-in.-diameter three-conductor sea cable. The system uses commercially available modules to provide Ethernet connectivity through existing sea cables, linking serial and video underwater instrumentation to the shipboard user. The new method applies Synchronous Digital Subscriber Line (SDSL) communications technology to undersea applications, greatly increasing the opportunities to use scientific instrumentation from existing ships and sea cables at minimal cost and without modification.

A New Method about Port Scan of Network Hosts

2011 ◽  
Vol 480-481 ◽  
pp. 190-194
Author(s):  
Xin Chao Han ◽  
Yong Qiang Ma
Keyword(s):  
Process Design ◽  
Design Thinking ◽  
Data Packet ◽  
Assessment System ◽  
New Method ◽  
Security Assessment ◽  
Test Results ◽  
Data Link ◽  
Link Layer ◽  
Data Link Layer

To resolve the question: accurately scanned the port of remote network host through firewall, researched deeply on the existing scan methods, found their weakness, provided a new method about port detection through firewall. Described the design thinking and scan process, design the first part of ETHERNET, send out and receive of the data packet was turned from the IP layer to data link layer. delivered the data packet directly to network hosts. At last, developed the network security assessment system based on this way. Test results show that this method can penetrate firewall and accurately scan the port of remote network host.

A New Method for Extracting the Hierarchical Organization of Networks

2017 ◽  
Vol 16 (05) ◽  
pp. 1359-1385 ◽  
Author(s):  
Weihua Zhan ◽  
Jihong Guan ◽  
Zhongzhi Zhang
Keyword(s):  
Complex Networks ◽  
Networked Systems ◽  
Hierarchical Organization ◽  
New Method ◽  
Network Data ◽  
Large Complex

Extracting the hierarchical organization of networks is currently a pressing task for understanding complex networked systems. The hierarchy of a network is essentially defined by the heterogeneity of link densities of communities at different scales. Here, we define a top-level partition (TLP) as a bipartition of the network (or a sub-network) such that no top-level community (TLC) runs across the two parts. It has been found that a TLP generally has a higher modularity than a non-top-level (TLP) partition when their TLCs have similar sizes and when the link densities of neighboring levels are well separated from each other. A spectral TLP procedure is proposed here to search for TLPs of a network (or sub-network). To extract the hierarchical organization of large complex networks, an algorithm called TLPA has been developed based on the TLP. Experiments have shown that the method developed in this research extract hierarchy accurately from network data.

A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit

2018 ◽  
Vol 13 (1) ◽  
pp. 25-39 ◽  
Author(s):  
Wei Feng ◽  
Yuqin Wu ◽  
Yexian Fan
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Security ◽  
Recurrent Neural Network ◽  
New Method ◽  
Series Data ◽  
External Information ◽  
Network Data ◽  
Content Type ◽  
Gated Recurrent Unit

Purpose The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations (NSS). Because the conventional methods for the prediction of NSS, such as support vector machine, particle swarm optimization, etc., lack accuracy, robustness and efficiency, in this study, the authors propose a new method for the prediction of NSS based on recurrent neural network (RNN) with gated recurrent unit. Design/methodology/approach This method extracts internal and external information features from the original time-series network data for the first time. Then, the extracted features are applied to the deep RNN model for training and validation. After iteration and optimization, the accuracy of predictions of NSS will be obtained by the well-trained model, and the model is robust for the unstable network data. Findings Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models. Although the deep RNN models need more time consumption for training, they guarantee the accuracy and robustness of prediction in return for validation. Originality/value In the prediction of NSS time-series data, the proposed internal and external information features are well described the original data, and the employment of deep RNN model will outperform the state-of-the-arts models.

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