A rapid detection algorithm for malformed H-DoS in the cloud platform

2013 ◽  
Vol 5 (9) ◽  
pp. 474-481 ◽  
Author(s):  
Yulong Wang ◽  
Fengfeng Wang ◽  
Jing Guo
Keyword(s):  
Rapid Detection ◽  
Detection Algorithm ◽  
Cloud Platform

Rapid Detection Algorithm for Small Objects Based on Receptive Field Block

2020 ◽  
Vol 57 (2) ◽  
pp. 021501
Author(s):  
王伟锋 Wang Weifeng ◽  
金杰 Jin Jie ◽  
陈景明 Chen Jingming
Keyword(s):  
Receptive Field ◽  
Rapid Detection ◽  
Detection Algorithm

scholarly journals An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Liu ◽  
Shuyu Chen ◽  
Zhen Zhou ◽  
Tianshu Wu
Keyword(s):  
Anomaly Detection ◽  
Virtual Machine ◽  
Large Scale ◽  
Virtual Machines ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Platform ◽  
Self Organizing Maps ◽  
Detection Speed ◽  
Self Organizing

Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

scholarly journals Application of the TRUFAS detection algorithm to the first two runs of CoRoT

2008 ◽  
Vol 4 (S253) ◽  
pp. 374-377
Author(s):  
Clara Régulo ◽  
Jose M. Almenara ◽  
Hans J. Deeg
Keyword(s):  
Fast Algorithm ◽  
Rapid Detection ◽  
Detection Algorithm ◽  
Space Mission ◽  
Eclipsing Binaries ◽  
Long Run ◽  
Planetary Transits

AbstractTRUFAS is a wavelet-based algorithm developed for the rapid detection of planetary transits in the frame of the COROT space mission. We present the application of this algorithm to the first two observing fields of CoRoT data. In these, CoRoT has observed a total of about 20000 stars. The first CoRoT observing run, IRa01, covers 2 months, February and March 2007, followed by the 5-months long run LRc01. TRUFAS is a very fast algorithm delivering reliable detections. Here we show the results when TRUFAS was applied to these first two sets of data. In the first run, IRa01, TRUFAS found 10 planet candidates and 143 eclipsing binaries and in the LRc01 10 planet candidates and 124 binaries, with a processing that lasted only one night.

A rapid detection algorithm of corrupted data in cloud storage

2018 ◽  
Vol 111 ◽  
pp. 115-125 ◽  
Author(s):  
Guangwei Xu ◽  
Zhifeng Sun ◽  
Cairong Yan ◽  
Yanglan Gan
Keyword(s):  
Rapid Detection ◽  
Cloud Storage ◽  
Detection Algorithm

Research on Rapid Detection Algorithm Based on Property Choosing Tree

2012 ◽  
Vol 505 ◽  
pp. 386-392
Author(s):  
Neng Shan Feng ◽  
Zhong Ming Yang
Keyword(s):  
Decision Tree ◽  
Rapid Detection ◽  
Detection Algorithm ◽  
Construction Method ◽  
The State ◽  
Transport Layer ◽  
Network Protocol ◽  
Protocol Stack ◽  
Link Layer ◽  
Network Layer

The construction method used by detection engine Snort-NG based on ID3 decision tree has the problem of excessive memory occupancy. The idea that the test properties are chosen according to the gradation of rule property in network protocol stack was presented in this paper; that is, the property of link layer first determined, and then network layer and transport layer. The atomicity of the value of these properties were preserved and the values of these properties were treated as a whole. The results of experiment showed that the occupancy of memory was much less in the state of non-trivial property being very common with this approach.

Search mode for rapid detection of virus particles

1991 ◽  
Vol 49 ◽  
pp. 286-287
Author(s):  
O. E. Bradfute
Keyword(s):  
Electron Microscopy ◽  
Rapid Detection ◽  
Plant Virus ◽  
Low Dose ◽  
Search Mode ◽  
Virus Diseases ◽  
Virus Particles ◽  
Focus Image ◽  
Time Required ◽  
Rapid Switching

Electron microscopy is frequently used in preliminary diagnosis of plant virus diseases by surveying negatively stained preparations of crude extracts of leaf samples. A major limitation of this method is the time required to survey grids when the concentration of virus particles (VPs) is low. A rapid survey of grids for VPs is reported here; the method employs a low magnification, out-of-focus Search Mode similar to that used for low dose electron microscopy of radiation sensitive specimens. A higher magnification, in-focus Confirm Mode is used to photograph or confirm the detection of VPs. Setting up the Search Mode by obtaining an out-of-focus image of the specimen in diffraction (K. H. Downing and W. Chiu, private communications) and pre-aligning the image in Search Mode with the image in Confirm Mode facilitates rapid switching between Modes.

Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 238-239
Author(s):  
C.D. Humphrey ◽  
T.L. Cromeans ◽  
E.H. Cook ◽  
D.W. Bradley
Keyword(s):  
Electron Microscopy ◽  
Liquid Phase ◽  
Cell Cultures ◽  
Rapid Detection ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Solid Phase ◽  
Immune Electron Microscopy ◽  
Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

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